• W. Ching and M. Ng, Basic Elements and Models of Management Mathematics, GBS Publishers (2007), 230 pages
• T. Park, S. Tsui, L. Chen, M. Ng, L. Wong, X. Hu: 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, Hong Kong, China, 18 - 21 December 2010, Proceedings IEEE Computer Society 2010 [ISBN 978-1-4244-8305-1]
• W. Liu, M. Ng and Z. Shi, Recent Progress in Scientific Computing, Science Press (2007), 401 pages [ISBN 978-7-03-019043-7]
• W. Ching and M. Ng, Markov Chains: Models, Algorithms and Applications, International Series on Operations Research and Management Science, Springer (2006), 205 pages [ISBN 0-387-29335-3]
• M. Ng, A. Doncescu, L. Yang and T. Leng, High Performance Computational Science and Engineering, IFIP TC5 Workshop on High Performance Computational Science and Engineering, Springer (2005), 238 pages [ISBN 0-387-24048-9]
• M. Ng, Iterative Methods for Toeplitz Systems, Oxford University Press (2004), 360 pages [ISBN 0-19-850420-9]
• W. Ching and M. Ng, Advances in Data Mining and Modeling, World Scientific Press (2003), 181 pages. ISBN 981-238-354-9.
• J. Fong and M. Ng, The Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Industrial Track and Workshops Proceeding, Printed by the the City University of Hong Kong, 2001. ISBN 962-442-187-0.

• Numerical Linear Algebra with Applications: The Special Issue for Dedication to Robert J. Plemmons, 2015.
• Numerical Linear Algebra with Applications: The Special Issue of the Spectral Theory of Tensors and Its Applications, Volume 20(6), 2013.
• IEEE/ACM Transactions on Computational Biology and Bioinformatics: Guest Editorial: Bioinformatics and Computational Systems Biology, Volume 9(4), pp. 945-946, 2012.
• Numerical Linear Algebra with Applications: The Special Issue for the 3rd International Conference on Structured Matrices and Tensors, Volume 19(1), 2012.
• Journal of Computational Mathematics: The Special Issue for Joint China-Russia Conference on Computational Mathematics, Volumes 29(6) 2011, and 30(1), 2012.
• Numerical Mathematics: Theory, Methods and Applications: Special Issue for Image Processing and Numerical Linear Algebra, Volume 2, No. 4, 2009
• Linear Algebra and Its Applications: The Special Issue on Structured Matrices, Volume 428, Issues 2+3, 2008
• Multidimensional Systems and Signal Processing: Special Issue on Superresolution Imaging: Theory, Algorithms and Applications, Volume 18, Number 2/3, 2007
• Journal of Computational Mathematics: The Summer Workshop on Numerical Linear Algebra (2005), Volume 25, Number 5, 2007
• EURASIP Journal on Advances in Signal Processing: Super-Resolution Imaging: Analysis, Algorithms and Applications, Volume 2006, 2006
• Numerical Linear Algebra with Applications: Special Issue: Structured Matrices with Applications, Volume 12, Issue No. 2-3, 2005
• International Journal of Imaging Systems and Technology: The Special Issue: High-Resolution Imaging Reconstruction I and II, Volume 14, Nos. 2 and 3, 2004
• International Journal of Applied Mathematics: ``OR and Models'', Volume 13, No. 1, 2003

(Numerical Linear Algebra):

• Z. Zhu, H. Yan and M. Ng, The Use of Forecast Gradients in 3DVar Data Assimilation, Applied Mathematical Modelling
• Z. Jia, M. Ng and G. Song, Robust Quaternion Matrix Completion with Applications to Image Inpainting, Numerical Linear Algebra with Applications
• X. Lin, M. Ng and H. Sun, A Fast Solver for Multidimensional Time-Space Fractional Diffusion Equation with Variable Coefficients, Computers and Mathematics with Applications
• W. Li, R. Ke, M. Ng, W. Ching, A C-Eigenvalue Problem for Tensors with Applications to Higher-order Multivariate Markov Chains, Computers and Mathematics with Applications
• X. Zhang and M. Ng, A Fast Algorithm for Solving Linear Inverse Problems with Uniform Noise Removal, Journal of Scientific Computing
• Z. Fang, M. Ng, H. Sun, Z. Chen, Circulant Preconditioners for a Kind of Spatial Fractional Diffusion Equation, Numerical Algorithms
• Z. Jia, M. Ng and G. Song, Lanczos Method for Large-Scale Quaternion Singular Value Decomposition, Numerical Algorithms
• X. Lin, M. Ng and H. Sun, A Separable Preconditioner for Time-Space Fractional Caputo-Riesz Diffusion Equations, Numer. Math. Theor. Meth. Appl.
• J. Pan and M. Ng, Symmetric Orthogonal Approximation to Symmetric Tensors with Applications to Image Reconstruction, Numerical Linear Algebra with Applications
• J. Pan and M. Ng, Orthogonal Nonnegative Matrix Factorization by Sparsity and Nuclear Norm Optimization, SIAM Journal on Matrix Analysis and Applications, V39 (2018), pp. 856-875
• D. Liu, W. Li, M. Ng and S. Vong, Uniqueness and Perturbation Bounds for Sparse Non-negative Tensor Equations, Frontiers of Mathematics in China
• M. Ng and Z. Zhu, Sparse Matrix Computation for Air Quality Forecast Data Assimilation, Numerical Algorithms
• X. Lin, M. Ng and H. Sun, Efficient Preconditioner of One-sided Space Fractional Diffusion Equation, BIT Numerical Mathematics
• W. Ding, M. Ng and Y. Wei, Fast Computation of Stationary Joint Probability Distribution of Sparse Markov Chains, Applied Numerical Mathematics, V125 (2018), pp. 68-85
• X. Lin, M. Ng and H. Sun, A Splitting Preconditioner for Toeplitz-Like Linear Systems Arising from Fractional Diffusion Equations, SIAM Journal on Matrix Analysis and Applications, V38 (2017), pp. 1580-1614
• W. Li, D. Liu, M. Ng, S. Vong, The Bounds for Uniqueness of Multilinear PageRank Vector, Numerical Linear Algebra with Applications
• X. Lin, M. Ng and H. Sun, A Multigrid Method for Linear Systems Arising from Time-Dependent Two-Dimensional Space-Fractional Diffusion Equations, Journal of Computational Physics, V336 (2017), pp. 69-86
• V. Rao, A. Sandu, M. Ng and E. Nino-Ruiz, Robust Data Assimilation Using L1 and Huber Norms, SIAM Journal on Scientific Computing, V39 (2017), pp. 548-570
• H. Fu, M. Mg and H. Wang, A Divide-and-conquer Fast Finite Fifference Method for Space-time Fractional Partial Differential Equation, Computers and Mathematics with Applications, V73 (2017), pp. 1233-1242
• C. Chuan, M. Ng and S. Zhang, Block Spectral Clustering Methods for Multiple Graphs, Numerical Linear Algebra with Applications, V24 (2017)
• J. Pan, M. Ng and H. Wang, Fast Preconditioned Iterative Methods for Finite Volume Discretization of Steady-State Space-fractional Diffusion Equations, Numerical Algorithms, V74 (2017), pp. 153-173
• R. Ke, W. Li and M. Ng, Numerical Ranges of Tensors, Linear Algebra and its Applications, V508 (2016), pp. 100-132
• J. Pan, M. Ng and H. Wang, Fast Iterative Solvers for Linear Systems Arising from Time-Dependent Space-Fractional Diffusion Equations, SIAM Journal on Scientific Computing, V38 (2016), pp. 2806-2826
• X. Zhang, L. Cheng, D. Chu, L. Liao, M. Ng and R. Tan, Incremental Regularized Least Squares for Dimensionality Reduction of Large-scale Data, SIAM Journal on Scientific Computing, V38 (2016), pp. 414-439
• L. Cui, C. Chen, W. Li and M. Ng, An Eigenvalue Problem for Even Order Tensors with its Applications, Linear and Multilinear Algebra, V64 (2016)
• C. Chen, X. Li, M. Ng and X. Yuan, Total Variation Based Tensor Decomposition for Multi-Dimensional Data with Time Dimension, Numerical Linear Algebra with Applications, V22 (2015), pp. 999-1019
• L. Cui, W. Li and M. Ng, Primitive Tensors and Directed Hypergraphs, Linear Algebra and its Applications, V471 (2015), pp. 96-108
• W. Li and M. Ng, The Perturbation Bound for the Spectral Radius of a Nonnegative Tensor, Advances in Numerical Analysis, Volume 2014 (2014), Article ID 109525
• W. Li and M. Ng, Some Bounds for the Spectral Radius of Nonnegative Tensors, Numerische Mathematik, V130 (2015), pp. 315-335
• X. Li and M. Ng, Solving Sparse Non-Negative Tensor Equations: Algorithms and Applications, Frontiers of Mathematics in China, V10 (2015), pp. 649-680
• Q. Niu and M. Ng, Numerical Studies of a Class of Composite Preconditioners, Journal of Computational Mathematics, V32 (2014), pp. 136-151
• J. Pan, R. Ke, M. Ng and H. Sun, Preconditioning Techniques for Diagonal-Times-Toeplitz Matrices in Fractional Diffusion Equations, SIAM Journal on Scientific Computing, V36 (2014), pp. 2698-2719
• L. Cui, W. Li and M, Ng, Birkhoff-von Neumann Theorem for Multi-Stochastic Tensors, SIAM Journal on Matrix Analysis and Applications, V35 (2014), pp. 956-973
• X. Chen, W. Li and M. Ng, On Condition Numbers of the Spectral Projections Associated with Periodic Eigenproblems, Journal of Computational and Applied Mathematics, V272 (2014), pp. 417-429
• W. Li, L. Cui and M. Ng, The Perturbation Bound for the Perron Vector of a Transition Probability Tensor, Numerical Linear Algebra with Applications, V20 (2013), pp. 985-1000
• W. Li and M. Ng, On the Limiting Probability Distribution of a Transition Probability Tensor, Linear and Multilinear Algebra, V62 (2014), pp. 362-385
• D. Chu, L. Liao and M. Ng, Sparse Orthogonal Linear Discriminant Analysis, SIAM Journal on Scientific Computing, V34 (2012), pp. 2421-2443
• W. Li, M. Ng and L. Cui, On Computation of the Steady-State Probability Distribution of Probabilistic Boolean Networks with Gene Perturbation, Journal of Computational and Applied Mathematics, V236 (2012), pp. 4067-4081
• X. Chen, W. Li and M. Ng, Perturbation Analysis for Anti-triangular Schur Decomposition, SIAM Journal on Matrix Analysis and Applications, V33 (2012), pp. 325-335
• G. Yin, J. Yin and M. Ng, On Adaptively Accelerated Arnoldi Method for Computing PageRank, Numerical Linear Algebra with Applications, V19 (2012), pp. 73-85
• L. Zhang, L. Liao and M. Ng, Superlinear Convergence of a General Algorithm for the Generalized Foley-Sammon Discriminant Analysis, Journal of Optimization Theory and Applications, V157 (2013), pp. 853-865
• Y. Wen, W. Ching and M. Ng, Approximate Inverse-Free Preconditioners for Toeplitz Matrices, Applied Mathematics and Computation, V217 (2011), pp. 6856-6867
• N. Mastronardi, M. Ng, E. Tyrtyshnikov, Decay in Functions of Multi-band Matrices, SIAM Journal on Matrix Analysis and Applications, V31 (2010), pp. 2721-2737
• M. Ng and J. Pan, Approximate Inverse Circulant-plus-Diagonal Preconditioners for Toeplitz-plus-Diagonal Matrices, SIAM Journal on Scientific Computing, V32 (2010), pp. 1442-1464
• Y. Huang and M. Ng, Product-Type Block Preconditioners for Electromagnetic Cavity Problems, Applied Mathematics and Computation, V216 (2010), pp. 1741-1751
• L. Zhang, L. Liao and M. Ng, Fast Algorithms for the Generalized Foley-Sammon Discriminant Analysis, SIAM Journal on Matrix Analysis and Applications, V31 (2010), pp. 1584-1605
• F. Lin and M. Ng, Inverse Product Toeplitz Preconditioners for non-Hermitian Toeplitz Systems, Numerical Algorithms, V54 (2010), pp. 279-295
• M. Ng, L. Qi and G. Zhou, Finding the Largest Eigenvalue of a Non-Negative Tensor, SIAM Journal on Matrix Analysis and Applications, V31 (2009), pp. 1090-1099
• Z. Bai, M. Ng and Z. Wang, Constraint Preconditioners for Symmetric Indefinite Matrices, SIAM Journal on Matrix Analysis and Applications, V31 (2009), pp. 410-433
• L. Chan, M. Ng and N. Tsing, Spectral Analysis for HSS Preconditioners, Numerical Mathematics: Theory, Methods and Applications, V1 (2008), pp. 57-77
• W. Ching, M. Ng and E. Fung, Higher-order Multivariate Markov Chains and their Applications, Linear Algebra and Its Applications, V428 (2008), pp. 492-507
• Z. Bai, G. Golub and M. Ng, On Inexact Hermitian and Skew-Hermitian Splitting Methods for Non-Hermitian Positive Definite Linear Systems, Linear Algebra and Its Applications, V428 (2008), pp. 413-440
• W. Ching, M. Ng and W. Youwei, Block Diagonal and Schur Complement Preconditioners for Block Toeplitz Systems with Small Size Blocks, SIAM Journal on Matrix Analysis and Applications, V29 (2007), pp. 1101-1119
• Y. Wei, N. Zhang, M. Ng and W. Xu, Tikhonov Regularization for Weighted Total Least Squares Problems, Applied Mathematics Letters, V20 (2007), pp. 82-87
• L. Lu and M. Ng, Maximum Inverse Positive Matrices, Applied Mathematics Letters, V20 (2007), pp. 65-69
• Z. Bai, G. Golub and M. Ng, On Successive-Overrelaxation Acceleration of the Hermitian and Skew-Hermitian Splitting Iterations, Numerical Linear Algebra with Applications, V14 (2007), pp. 319-335
• M. Benzi and M. Ng, Preconditioned Iterative Methods for Weighted Toeplitz Least Squares Problems, SIAM J. Matrix Analysis and Applications, V27 (2006), pp. 1106-1124
• J. Cai, M. Ng and Y. Wei, Computing the Group Inverse of Singular Toeplitz Matrix, Journal of Computational Mathematics, V24 (2006), pp. 647-656
• J. Pan, M. Ng and Z. Bai, New Preconditioners for Saddle Point Problems, Applied Mathematics and Computation, V172 (2006), pp. 762-771
• W. Ching, M. Ng and W. Yuen, A Direct Method for Solving Block-Toeplitz with Near-Circulant-Block Systems with Applications to Hybrid Manufacturing Systems, Numerical Linear Algebra with Applications, V12 (2005), pp. 957-966
• L. Lu and M. Ng, Effects of a Parameter on a Nonsymmetric Algebraic Riccati Equation, Applied Mathematics and Computation, V172 (2006), pp. 753-761
• M. Ho and M. Ng, Splitting Iterations for Circulant-plus-diagonal Systems, Numerical Linear Algebra with Applications, V12 (2005), pp. 779-792
• Y. Wei, H. Diao and M. Ng, On Drazin Inverse of Singular Toeplitz Matrix, Applied Mathematics and Computation, V172 (2006), pp. 809-817
• Z. Bai and M. Ng, On Inexact Preconditioners for Nonsymmetric Matrices, SIAM Journal on Scientific Computing, V26 (2005), pp. 1710-1724
• A. Ho and M .Ng, Iterative Methods for Robbins Problems, Applied Mathematics and Computation, V165 (2005), pp. 103-125
• M. He, L. Lu and M. Ng, Toeplitz and Positive Semidefinite Complete Problem for Cycle Graph, Numer. Math.,  J. Chinese Univs., V14 (2005), pp. 67-78
• M. Ng, S. Serra and C. Tablino-Possio, Multigrid Methods for Symmetric Sinc-Galerkin Systems, Numerical Linear Algebra with Applications, V12 (2005), pp. 261-269
• F. Lin, M. Ng and W. Ching, Factorized Banded Inverse Preconditioners for Matrices with Toeplitz Structure, SIAM Journal on Scientific Computing, V26 (2005) pp. 1852-1870
• Y. Wei and M. Ng, Displacement Structure of Group Inverses, Numerical Linear Algebra with Applications, V12 (2005), pp. 103-110
• M. Ng, Stefano Serra-Capizzano and Cristina Tablino-Possio, Multigrid Preconditioners for Symmetric Sinc Systems, ANZIAM J. V45(E), 2004, pp. 857-869
• Y. Wen, M. Ng, W. Ching, H. Liu, A Note on the Stability of Toeplitz Matrix Inversion Formulas, Applied Mathematics Letter, V17 (2004), pp. 903-907
• W. Ching, W. Yuen and M. Ng, A Hybrid Algorithm for Queueing Systems, CALCOLO, V41 (2004), pp. 139-151
• L. Lu, W. Ching and M. Ng, Exact Algorithms for Singular Tridiagonal Systems with Applications to Markov Chains, Applied Mathematics and Computation, V159 (2004) pp. 275-289
• L. Lu and M. Ng, Localization of Perron Roots, Linear Algebra and its Applications, V392 (2004), pp. 103-117
• L. Lu and M. Ng, On Sufficient and Necessary Conditions for the Jacobi Matrix Inverse Eigenvalue Problem, Numer. Math., V98 (2004), pp. 167-176
• Y. Wei and M. Ng, Weighted Tikhonov Filter Matrices for Ill-Posed Problems, Applied Mathematics and Computation, V149 (2004), pp. 411-422
• Y. Wei, J. Cai and M. Ng, Computing Moore-Penrose Inverses of Toeplitz Matrices by Newton's Iteration, Mathematical and Computer Modeling, V40 (2004), pp. 181-191
• L. Lu and M. Ng, Symmetric Versions of Sign-Function Algoriothm for Discrete-time Algebraic Riccati Equations, International Journal of Applied Mathematics, V13 (2003), pp. 100-119
• F. Lin, W. Ching and M. Ng, Accurate and Fast Inversion Methods for Triangular Toeplitz Matrices, Theoretical Computer Science, V43 (2003), pp. 901-914
• F. Lin, W. Ching and M. Ng, Discrete Wavelet Transforms for Toeplitz Matrices, Linear Algebra Appls., V370 (2003), pp. 269-285
• Z. Bai, G. Golub and M. Ng, Hermitian and Skew-Hermitian Splitting Methods for Non-Hermitian Positive Definite Linear Systems, SIAM Journal on Matrix Analysis and Applications, V24 (2003), pp. 603-626
• M. Ng, X. Jin and H. Sun, Recursive-Based PCG Methods for Toeplitz Systems with Nonnegative Generating Functions, SIAM Journal on Scientific Computing, V24 (2003), pp. 1507-1529
• M. Ng, Circulant and Skew-Circulant Splitting Methods for Toeplitz Systems, Journal of Computational Applied Math., V159 (2003), pp. 101-108
• Y. Wen, W. Ching and M. Ng, Explicit Preconditioners for Toeplitz Systems, International Journal of Applied Mathematics, V13 (2003), pp. 72-83
• Z. Bai and M. Ng, Preconditioners for Nonsymmetric Block-Toeplitz-Like-Plus-Diagonal Linear System, Numer. Math., V96 (2003), pp. 197-220
• M. Ng and Z. Bai, A Hybrid Preconditioner of Banded Matrix Approximation and Alternating Direction Implicit Iteration for Symmetric Sinc-Galerkin Linear Systems, Linear Algebra Appls., V366 (2003), pp. 317-335
• M. Ng and D. Potts, Fast Iterative Methods for Sinc Systems, SIAM Journal on Matrix Analysis and Applications, V24 (2003), pp. 581-598
• M. Ng, K. Rost and Y. Wen, On Inversion of Toeplitz Matrices, Linear Alg. and Applications, V348 (2002), pp. 145-151
• R. Chan, M. Ng and A. Yip, The Best Circulant Preconditioners for Hermitian Toeplitz Systems II: The Multiple-Zero Case, Numer. Math., V92 (2002), pp. 17-40
• M. Ng and D. Potts, Circulant Preconditioners for Indefinite Toeplitz Systems, BIT, V41 (2001), pp. 1079-1088
• R. Chan, A. Yip and M. Ng, The Best Circulant Preconditioners for Hermitian Toeplitz Matrices, SIAM Journal on Numerical Analysis, V38 (2001), pp. 876-896
• M. Ng, Preconditioned Lanczos Methods for the Minimum Eigenvalue of a Symmetric Positive Definite Toeplitz Matrix, SIAM Journal on Scientific Computing, V21 (2000), pp. 1973-1986
• T. Chan and M. Ng, Galerkin Projection Methods for Solving Multiple Linear Systems, SIAM Journal on Scientific Computing, V21 (2000), pp. 836-850
• M. Ng, Fast Iterative Methods for Symmetric Sinc-Galerkin Systems, IMA Journal of Numerical Analysis, V19 (1999), pp. 357-373
• H. Chan, M. Ng and R. Chan, Band Toeplitz Preconditioners for Toeplitz Systems with Rational Generating Functions, The Bulletin of Hong Kong Mathematical Society, V1 (1997), pp. 67-86
• M. Ng, Band Preconditioners for Block-Toeplitz-Toeplitz-Block Systems, Linear Algebra and its Applications, V259 (1997), pp. 307-327
• R. Chan and M. Ng, Scientific Applications of Iterative Toeplitz Solvers, CALCOLO, V33 (1996), pp. 249-267
• R. Chan and M. Ng, Conjugate Gradient Methods for Toeplitz Systems, SIAM Review, V38 (1996), pp. 427-482
• R. Chan, C. Wong and M. Ng, Sine-transform Based Preconditioners For Symmetric Toeplitz Systems, Linear Algebra and its Applications, V232 (1996), pp. 237-259
• R. Chan and M. Ng, Toeplitz Preconditioners for Hermitian Toeplitz Systems, Linear Algebra and its Applications, V190 (1993), pp. 181-208
• R. Chan and M. Ng, Fast Iterative Solver for Toeplitz-plus-Band Systems, SIAM Journal on Scientific Computing, V14 (1993), pp. 1013-1019

• X. Zhang and M. Ng, A Corrected Tensor Nuclear Norm Minimization Method for Noisy Low-Rank Tensor Completion, SIAM Journal on Imaging Sciences
• Z. Jia, M. Ng and W. Wang, Color Image Restoration by Saturation-Value (SV) Total Variation, SIAM Journal on Imaging Sciences
• W. Wang, F. Li and M. Ng, Structural Similarity based Nonlocal Variational Models for Image Restoration, IEEE Transactions on Image Processing
• F. Li and M. Ng, Image Colorization by Using Graph BiLaplacian, Advances in Computational Mathematics
• Z. Jin, L. Min, M. Ng and M. Zheng, Image Colorization by Fusion of Color Transfers based on DFT and Variance Features, Computers and Mathematics with Applications
• W. Wang, N. Sun and M. Ng, A Variational Gamma Correction Model for Image Contrast Enhancement, Inverse Problems and Imaging
• Y. Wen, H. Sun and M. Ng, A Primal-Dual Method for Meyer Model of Cartoon and Texture Decomposition, Numerical Linear Algebra with Applications
• M. Cheng, J. Ling and M. Ng, Tensor-based Low-dimensional Representation Learning for Multi-view Clustering, IEEE Transactions on Image Processing
• G. Cheung, E. Magli, Y. Tanaka and M. Ng, Graphs Spectral Image Processing, Proceedings of the IEEE, V106 (2018), pp. 907-930
• C. Liu, M. Ng, T. Zeng, Weighted Variational Model for Selective Image Segmentation with Application to Medical Images, Pattern Recognition, V76 (2018), pp. 367-379
• X. Zhang, M. Bai and M. Ng, A Fast Algorithm for Deconvolution and Poisson Noise Removal, Journal of Scientific Computing, V75 (2018), pp. 1535-1554
• Y. Wen, W. Ching and M. Ng, A Semi-Smooth Newton Method for Inverse Problem with Uniform Noise, Journal of Scientific Computing, V75 (2018), pp. 713-732
• M. Ng, H. Ngan, X. Yuan and W. Zhang, Lattice-based Patterned Fabric Inspection by Using Total Variation with Sparsity and Low-Rank Representations, SIAM Journal on Imaging Sciences, V10 (2017), pp. 2140-2164
• X. Li, M. Ng, Y. Ye, E. Wang and X. Xu, Block Linear Discriminant Analysis for Visual Tensor Objects with Frequency or Time Information, Journal of Visual Communication and Image Representation, V49 (2017), pp. 38-46
• L. Jing, C. Shen, L. Yang, J. Yu and M. Ng, Multi-label Classification by Semi-supervised Singular Value Decomposition, IEEE Transactions on Image Processing, V26 (2017), pp. 4612-4625
• X. Zhang, M. Bai and M. Ng, Nonconvex-TV Based Image Restoration with Impulse Noise Removal, SIAM Journal on Imaging Sciences, V10 (2017), pp. 1627-1667
• W. Wei, X. Zhao and M. Ng, A Variational Approach for Restoring Images Corrupted by Noisy Blur Kernels and Additive Noise, Numerical Linear Algebra with Applications,
• M. Ng, Q. Yuan, L. Yan and J. Sun, An Adaptive Weighted Tensor Completion Method for the Recovery of Remote Sensing Images With Missing Data, IEEE Transactions on Geoscience and Remote Sensing, V55 (2017), pp. 3367-3381
• X. Zhang, B. Javidi, M. Ng, Automatic Regularization Parameter Selection by Generalized Cross-validation for Total Variational Poisson Noise Removal, Applied Optics, V56 (2017), pp. D47-D51
• L. Pi, W. Wang and M. Ng, A Spatially Variant Total Variational Model for Chromatic Aberration Correction, Journal of Visual Communication and Image Representation, V41 (2016), pp. 296-304
• H. Chang, Y. Lou, M. Ng and T. Zeng, Phase Retrieval from Incomplete Magnitude Information via Total Variation Regularization, SIAM Journal on Scientific Computing, V38 (2016), pp. 3672-3695
• W. Wei and M. Ng, Convex Regularized Inverse Filtering Methods for Blind Image Deconvolution, Signal, Image and Video Processing, V10 (2016), pp. 1353-1360
• W. Fan, X. Zhao and M. Ng, Multiplicative Noise and Blur Removal by Framelet Decomposition and l1-based L-curve Method, IEEE Transactions on Image Processing, V25 (2016), pp. 4222-4232
• L. Yang, M. Ng, J. Yu and L. Jing, A Discriminative and Sparse Topic Model for Image Classification and Annotation, Image and Vision Computing, V51 (2016), pp. 22-35
• Z. Jin, C. Zhou and M. Ng, A Coupled Total Variation Model with Curvature Driven for Image Colorization, Inverse Problems and Imaging, V10 (2016), pp. 1037-1055
• X. Li, M. Ng and Y. Ye, MultiVCRank with Applications to Image Retrieval, IEEE Transactions on Image Processing, V25 (2016), pp. 1396-1409
• W. Wang, C. Chen and M. Ng, An Image Pixel Based Variational Model for Histogram Equalization, Journal of Visual Communication and Image Representation, V34 (2016), pp. 118-134
• L. Yang, L. Jing and M. Ng, Robust and Non-Negative Collective Matrix Factorization for Text-to-Image Transfer Learning, IEEE Transactions on Image Processing, V24 (2015), pp. 4701-4714
• X. Li, M. Ng and X. Yuan, Median Filtering Based Methods for Static Background Extraction from Surveillance Video, Numerical Linear Algebra with Applications, V22 (2015), pp. 845-865
• W. Wang, X. Zhao and M. Ng, A Cartoon-plus-Texture Image Decomposition Model for Blind Deconvolution, Multidimensional Systems and Signal Processing, V27 (2016), pp. 541-562
• J. Xiao, M. Ng and Y. Yang On the Convergence of Nonconvex Minimization Methods for Image Recovery, IEEE Transactions on Image Processing, V24 (2015), pp. 1587-1598
• H. Chang, M. Ng, W. Wang, T. Zeng, Retinex Image Enhancement via a Learned Dictionary, Optical Engineering, 54(1) (2015), 013107
• M. Qiao and M. Ng, Tone Mapping for High Dynamic Range Images Using Localized Gamma Correction, Journal of Electronic Imaging, V24 (2015), 013010
• C. Chen, M. Ng and X. Zhao, Alternating Direction Method of Multipliers for Nonlinear Image Restoration Problems, IEEE Transactions on Image Processing, V24 (2015), pp. 33-43
• Z. Jin and M. Ng, A Contrast Maximization Method for Color-to-Grayscale Conversion, Multidimensional Systems and Signal Processing, V26 (2015), pp. 869-877
• M. Ng, H. Ngan, X. Yuan and W. Zhang, Patterned Fabric Inspection and Visualization by the Method of Image Decomposition, IEEE Transactions on Automation Science and Engineering, V11 (2014), pp. 943-947
• W. Wang and M. Ng, A Nonlocal Total Variation Model for Image Decomposition: Illumination and Reflectance, Numerical Mathematics: Theory , Methods and Applications, V7 (2014), pp. 334-355
• Z. Jin, F. Li and M. Ng, A Variational Approach for Image Decolorization By Variance Maximization, SIAM Journal on Imaging Sciences, V7 (2014), pp. 944-968
• M. Ng and J. Pan, Weighted Toeplitz Regularized Least Squares Computation for Image Restoration, SIAM Journal on Scientific Computing, V36 (2014), pp. 94-121
• L. Jing and M. Ng, Sparse Label-Indicator Optimization Methods for Image Classification, IEEE Transactions on Image Processing, V23 (2014), pp. 1002-1014
• X. Zhao, F. Wang and M. Ng, A New Convex Optimization Model for Multiplicative Noise and Blur Removal, SIAM Journal on Imaging Sciences, V7 (2014), pp. 456-475
• H. Chang, M. Ng and T. Zeng, Reducing Artifact in JPEG Decompression via a Learned Dictionary, IEEE Transactions on Signal Processing, V62 (2014), pp. 718-728
• M. Qiao, W. Wang, M. Ng, Multiphase Texture Segmentation Using Gabor Features Histograms Based on Wasserstein Distance, Communications in Computational Physics, V15 (2014), pp. 1480-1500.
• X. Zhao, W. Wang, T. Zeng, T. Huang and M. Ng, Total Variation Structured Total Least Squares Method for Image Restoration, SIAM Journal on Scientific Computing, V35 (2013), pp. 94-121
• W. Wang and M. Ng, A Variational Histogram Equalization Method for Image Contrast Enhancement, SIAM Journal on Imaging Sciences, V6 (2013), pp. 1345-1366
• M. Ng, C. Tam and F. Wang, Multi-view Foreground Segmentation via Fourth Order Tensor Learning, Inverse Problems and Imaging, V7 (2013), pp. 885-906
• M. Ng and W. Wang A Variational Approach for Image Stitching II: Using Image Gradients, SIAM Journal on Imaging Sciences, V6 (2013), pp. 1345-1366
• W. Wang and M. Ng, A Variational Approach for Image Stitching, SIAM Journal on Imaging Sciences, V6 (2013), pp. 1318-1344
• L. Ma, M. Ng, J. Yu and T. Zeng, Efficient Box-Constrained TV-Type-L22 Algorithms for Restoring Images with Impulse Noise, Journal of Computational Mathematics, V31 (2013), pp. 249-270
• M. Ng, X. Yuan and W. Zhang, A Coupled Variational Image Decomposition and Restoration Model for Blurred Cartoon-plus-Texture Images with Missing Pixels, IEEE Transactions on Image Processing, V22 (2013), pp. 2233-2246
• X. Zhao, F. Wang, T. Huang, M. Ng and R. Plemmons, Deblurring and Sparse Unmixing For Hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, V51 (2013), pp. 4045-4058
• M. Nikolova, M. Ng and C. Tam, On l_1 Data Fitting and Concave Regularization for Image Recovery, SIAM Journal on Scientific Computing, V35 (2013), pp. 397-430
• X. Chen, M. Ng and C. Zhang, Non-Lipschitz l_p-regularization and Box Constrained Model for Image Restoration, IEEE Transactions on Image Processing, V21 (2012), pp. 4709-4720
• L. Jing, C. Zhang and M. Ng, SNMFCA: Supervised NMF-based Image Classification and Annotation, IEEE Transactions on Image Processing, V21 (2012), pp. 4508-4521
• Y. Huang, L. Moisan, M. Ng and T. Zeng, Multiplicative Noise Removal via a Learned Dictionary, IEEE Transactions on Image Processing, V21 (2012), pp. 4534-4543
• F. Wang and M. Ng, A Fast Minimization Method for Blur and Multiplicative Noise Removal, International Journal of Computer Mathematics, V90 (2013), pp. 48-61
• Y. Law, H. Lee, M. Ng and A. Yip, A Semi-supervised Segmentation Model for Collections of Images, IEEE Transactions on Image Processing, V21 (2012), pp. 2955-2968
• W. Wang and M. Ng, A Variational Method for Blending of Multiple Images, IEEE Transactions on Image Processing, V21 (2012), pp. 1809-1822
• F. Li, M. Ng, R. Plemmons, Coupled Segmentation and Denoising/Deblurring Models for Hyperspectral Material Identification, Numerical Linear Algebra with Applications, V19 (2012), pp. 153-173
• W. Wang and M. Ng, On Algorithms for Automatic Deblurring From a Single Image, Journal of Computational Mathematics, V30 (2012), pp. 80-100
• M. Ng, F. Wang and X. Yuan, Inexact Alternating Direction Methods for Image Recovery, SIAM Journal on Scientific Computing, V33 (2011), pp. 1643-1668
• Y. Xiao, T. Zeng, J. Yu and M. Ng, Restoration of Images Corrupted by Mixed Gaussian-Impulse Noise via l_1-l_0 Minimization, Pattern Recognition, V44 (2011), pp. 1708-1720
• M. Ng and W. Wang, A Total Variation Model for Retinex, SIAM Journal on Imaging Sciences, V4 (2011), pp. 345-365
• H. Liao and M. Ng, Blind Deconvolution Using Generalized Cross Validation Approach to Regularization Parameter Estimation, IEEE Transactions on Image Processing, V20 (2011), pp. 670-680
• M. Ng, F. Wang and X. Yuan, Fast Minimization Methods for Solving Constrained Total-Variation Superresolution Image Reconstruction, Multidimensional Systems and Signal Processing, V22 (2011), pp. 259-286
• A. Yau, X. Tai and M. Ng, Compression and Denoising using l_0-norm, Journal Computational Optimization and Applications V50 (2011), pp. 425-444
• M. Ng, P. Weiss and X. Yuan, Solving Constrained Total-Variation Image Restoration and Reconstruction Problems via Alternating Direction Methods, SIAM Journal on Scientific Computing, V32 (2010), pp. 2710-2736
• F. Li, M. Ng and C. Li, Variational Fuzzy Mumford-Shah Model for Image Segmentation, SIAM Journal on Applied Mathematics, V70 (2010), pp. 2750-2770
• M. Nikolova, M. Ng and C. Tam, Fast Nonconvex Nonsmooth Minimization Methods for Image Restoration and Reconstruction, IEEE Transactions on Image Processing, V19 (2010), pp. 3073-3088
• M. Ng, G. Qiu and A. Yip, Numerical Methods for Interactive Multiple Class Image Segmentation Problems, International Journal of Imaging Systems and Technology, V20 (2010), pp. 191-201
• F. Li, M. Ng, T. Zeng and C. Shen, A Multiphase Image Segmentation Method Based on Fuzzy Region Competition, SIAM Journal on Imaging Sciences, V3 (2010), pp. 277-299
• T. Zeng, X. Li and M. Ng, Alternating Minimization Method for Total Variation Based Wavelet Shrinkage Model, Communications in Computational Physics, V8 (2010), pp. 976-994
• M. Poole, P. Weiss, H. Lopez, M. Ng and S. Crozler, Minimax Current Density Coil Design, Journal of Physics D: Applied Physics, V43 (2010), 095001
• K. Wong, C. Tam and M. Ng, Accurate Vascular Delay Estimation from Low Temporal Resolution Image Dataset, IEEE Signal Processing Magazine, V27 (2010), pp. 155-158
• F. Li and M. Ng, Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation, Communications in Computational Physics, V8 (2010), pp. 623-641
• Z. Bai, Y. Huang and M. Ng, Block-triangular Preconditioners fro Systems Arising From Edge-preserving Image Restoration, Journal of Computational Mathematics, V28 (2010), pp. 848-847
• Z. Bai, Y. Huang, M. Ng and X. Yang Preconditioned Iterative Methods for Algebraic Systems from Multiplicative Half-Quadratic Regularization Image Restorations, Numer. Math. Theor. Meth. Appl., V3 (2010), pp. 461-474
• F. Li, M. Ng and C. Shen, Multiplicative Noise Removal with Spatial-Varying Regularization Parameters, SIAM Journal on Imaging Sciences, V3 (2010), pp. 1-20
• T. Zeng and M. Ng, On the Total Variation Dictionary Model, IEEE Transactions on Image Processing, V19 (2010), pp. 821-825
• H. Liao, F. Li and M. Ng, On Selection of Regularization Parameter in Total Variation Image Restoration, Journal of the Optical Society of America A, V26 (2009), pp. 2311-2320
• Zheng-Jian Bai, M. Ng and and Liqun Qi, A Coordinate Gradient Descent Method for Nonsmooth Nonseparable Minimization, Numerical Mathematics: Theory, Methods and Applications, V2 (2009), pp. 377-402
• X. Guo, F. Li and M. Ng, A Fast l1-TV Algorithm for Image Restoration, SIAM Journal on Scientific Computing, V31 (2009), pp. 2322-2341
• Y. Huang, M. Ng and Y. Wen, Fast Image Restoration Methods for Impulse and Gaussian Noises Removal, IEEE Signal Processing Letters, V16 (2009), pp. 457-460
• H. Shen, M. Ng, R. Li and L. Zhang, Superresolution Reconstruction Algorithm to MODIS Remote Sensing Images, The Computer Journal, V52 (2009), pp. 90-100
• K. Wong, C. Tam, G. Young, M. Ng and S. Wong, Improved Residue Function and Reduced Flow Dependence in MR Perfusion Using Least-absolute-deviation Regularization, Magnetic Resonance in Medicine, V61 (2009), pp. 418-428
• Y. Huang, M. Ng and Y. Wen, A New Total Variation Method for Multiplicative Noise Removal, SIAM Journal on Imaging Sciences, V2 (2009), pp. 20-40
• L. Lu, M. Ng, F. Lin, Approximation BFGS Methods for Nonlinear Image Restoration, Journal of Computational and Applied Mathematics, 226 (2009), 84-91
• Y. Wen, M. Ng and Y. Huang, Efficient Total Variation Minimization Methods for Color Image Restoration, IEEE Transactions on Image Processing, V17 (2008), pp. 2081-2088
• Y. Huang, M. Ng and Y. Wen, A Fast Total Variation Minimization Method for Image Restoration, SIAM Journal on Multiscale Modeling and Simulation, V7 (2008), pp. 774-795
• Y. Wen, N. Ng and W. Ching, Iterative Algorithms Based on Decoupling of Deblurring and Denoising for Image Restoration, SIAM Journal on Scientific Computing, V30 (2008), pp. 2655-2674
• M. Nikolova, M. Ng, S.Zhang and W. Ching, Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization, SIAM Journal on Imaging Sciences, V1 (2008), pp. 2-25
• Y. Huang and M. Ng, Lipschitz and Total-Variational Regularization for Blind Deconvolution, Communications in Computational Physics, V4 (2008), pp. 195-206
• M. Ng, H. Shen, S. Chaudhuri, A. Yau, A Zoom Based Super-Resolution Reconstruction Approach Using Total Variation Prior, Optical Engineering, V46 (2007), 127003
• M. Ng, H. Shen, L. Zhang and E. Lam, A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video, EUASIP Journal on Advances in Signal Processing, V2007 (2007), Article ID 74585, 16 pages
• M. Ng, L. Qi, Y. Yang and Y. Huang, On Semismooth Newton's Methods for Total Variation Minimization, Journal of Mathematical Imaging and Vision, V27 (2007), pp. 265-276
• T. Chan, N. Ng, A. Yau and A. Yip, Superresolution Image Reconstruction Using Fast Inpainting Algorithms, Applied and Computational Harmonic Analysis, V23 (2007), pp. 3-24
• H. Fu, M. Ng and J. Barlow, Structured Total Least Squares for Color Image Restoration, SIAM Journal on Scientific Computing, V28 (2007), pp. 1100-1119
• W. Chan, E. Lam, M. Ng and G. Mak, Super-resolution Reconstruction in a Computational Compound-eye Imaging System, Multidimensional Systems and Signal Processing, V18 (2007), pp. 83-101
• A. Yau, N. Bose and M. Ng, An Efficient Algorithm for Superresolution in Medium Field Imaging, Multidimensional Systems and Signal Processing, V18 (2007), pp. 173-188
• J. Zhang, T. Zhou, C. Cheung, C. Beling, S. Fung, M. Ng, Deconvolution of 2D coincident Doppler broadening spectroscopy using the Richardson-Lucy algorithm, Nuclear Instruments and Methods in Physics Research A, V560 (2006), pp. 552-557
• N. Bose, M. Ng and A. Yau, A Fast Algorithm for Image Super-Resolution From Blurred Observations, EURASIP Journal on Applied Signal Processing, V2006 (2006), Article ID 35726, 14 pages
• H. Fu, M. Ng, M. Nikolova and J. Barlow, Efficient Minimization Methods of Mixed L1-L1 and L2-L1 Norms for Image Restoration, SIAM Journal on Scientific Computing, V27 (2006), pp. 1881-1902
• M. Nikolova and M. Ng, Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery, SIAM Journal on Scientific Computing, V27 (2006), pp. 937-966
• M. Ng and A. Yau, Super-resolution Image Restoration from Blurred Low-resolution Images, Journal of Mathematical Imaging and Vision, V23 (2005), pp. 367-378
• T. Woo, V. Cheng, C. Beling and M. Ng, An Evaluation of Algorithms for the Deconvolution of Doppler Broadening Positron Annihilation Radiation Spectroscopy Spectra, Computer Physics Communications, V168, Issue 3 (2005), pp. 177-186
• H. Ngan, G. Pang, S. Yung and M. Ng, Wavelet Based Methods for Patterned Fabric Defect Detection, Pattern Recognition, V38 (2005), pp. 559-576
• W. Ching, M. Ng, K. Sze and A. Yau, Super-Resolution Image Reconstruction Using Multisensors, Numerical Linear Algebra with Applications, V12 (2005), pp. 271-281
• M. Ng and W. Kwan, Image Restoration by Cosine Transform Based Iterative Regularization, Applied Mathematics and Computation, V160 (2005), pp. 499-515
• R. Peeters, P. Kornprobst, M. Nikolova, S. Sunaert, T. Vieville, G. Malandain, R. Deriche, O. Faugeras, M. Ng and P. Van Hecke, The Use of Superresolution Techniques to Reduce Slice Thickness in Functional MRI, International Journal of Imaging System and Technology, V14 (2004), pp. 131-138
• F. Lin, W. Ching and M. Ng, Preconditioning Regularized Least Squares Problems Arising from High-Resolution Image Reconstruction from Low-Resolution Frames, Linear Algebra and its Applications, V391 (2004), pp. 149-168
• M. Ng, S. Sze and S. Yung, Wavelet Algorithms for Deblurring Models, International Journal of Imaging System and Technology, V14(3) (2004), pp. 113-121
• Y. Wen, M. Ng and W. Ching, High-Resolution Image Reconstruction from Rotated and Translated Low-Resolution Images with Multisensors, International Journal of Imaging System and Technology, V14(2) (2004), pp. 75-83
• J. Nagy, M. Ng and L. Perrone, Kronecker Product Approximation for Image Restoration with Reflexive Boundary Conditions, SIAM Journal Matrix Analysis and Applications, V25 (2004), pp. 842-857
• W. Ching, M. Ng, K. Sze and A. Yau, Super-Resolution Image Restoration from Blurred Observations by Multisensors, International Journal of Imaging Systems and Technology, V13 (2003), pp. 153-160
• F. Lin and M. Ng, Super-Resolution Image Reconstruction with Estimation of Low-Resolution Frames, International Journal of Applied Mathematics, V13 (2003), pp. 84-99
• F. Ho, A. Ching, C. Beling, S. Fung, M. Ng, M. Biasini, G. Ferro, M. Gong, Positron-electron Autocorrelation Function Study of E-center in Silicon, Journal of Applied Physics, V94 (2003), pp. 5549-5555
• M. Ng and N. Bose, Mathematical Analysis of Super-Resolution Methodology, IEEE Signal Processing Magazine, V20 (2003), pp. 62-74
• K. Ho, C. Beling, S. Fung, K. Cheng, M. Ng and A. Yip, Deconvolution of Coincidence Doppler Broadening Spectra using Iterative Projected Newton Method with Non-negativity Constraints, Review of Scientific Instruments, V74 (2003), pp. 4779-4787
• M. Ng and N. Bose, Fast Color Image Restoration with Multisensors, International Journal of Imaging Systems and Technology, V12 (2003), pp. 189-197
• M. Ng and N. Bose, Analysis of Displacement Errors in High-Resolution Image Reconstruction with Multisensors, IEEE Transactions on Circuits and Systems: Part I, V49 (2002), pp. 806-813
• M. Ng, A Fast Algorithm for Blind Channel Identification, The Journal of Mathematical and Physical Sciences, V1 (2002), pp. 92-103
• M. Ng, J. Koo and N. Bose, Constrained Total Least Squares Computations for High Resolution Image Reconstruction with Multisensors, International Journal of Imaging Systems and Technology, V12 (2002), pp. 35-42
• M. Ng and W. Kwan, Comments on Least Squares Restoration of Multichannel Images, IEEE Transactions on Signal Processing, V49 (2001), p. 2885
• M. Ng and W. Kwan, High-Resolution Color Image Reconstruction with Neumann Boundary Conditions, Annals of Operations Research, V103 (2001), pp. 99-114
• M. Ng and A. Yip, A Fast MAP Algorithm for High-Resolution Image Reconstruction with Multisensors, Multidimensional Systems and Signal Processing, V12 (2001), pp. 143-164
• M. Ng, Robert Plemmons and Felipe Pimentel, A New Approach to Constrained Total Least Squares Image Restoration, Linear Algebra Appls., V316 (2000), pp. 237-258
• M. Ng, R. Chan, T. Chan and A. Yip, Cosine Transform Preconditioners for High Resolution Image Reconstruction, Linear Algebra Appls., V316 (2000), pp. 89-104
• M. Ng, R. Chan and W. Tang, A Fast Algorithm for Deblurring Models with Neumann Boundary Conditions, SIAM Journal on Scientific Computing, V21 (2000), pp. 851-866
• M. Ng, R. Plemmons and S. Qiao, Regularization of RIF Blind Image Deconvolution, IEEE Transactions on Image Processing, V9 (2000), pp. 1130-1134
• M. Ng, FFT--based Exponential-weighted Least Squares Computations, Linear Algebra and its Applications, V263 (1997), pp. 167-191
• M. Ng and R. Plemmons, LMS-Newton Adaptive Filtering Using FFT, SEAMS Math. Bulletin, V20 (1996), pp. 47-54
• M. Ng and R. Plemmons, Fast RLS Adaptive Filtering by FFT--based Conjugate Gradient Iterations, SIAM Journal on Scientific Computing, V17 (1996), pp. 920-941
• M. Ng and R. Plemmons, LMS-Newton Adaptive Filtering Using FFT--based Conjugate Gradient Iterations, Electronic Transactions on Numerical Analysis, V4 (1996), pp. 14-36
• R. Chan, M. Ng and R. Plemmons, Generalization of Strang's Preconditioner with Applications to Toeplitz Least Squares Problems, Numerical Linear Algebra with Applications, V3 (1996), pp. 45-64
• M. Ng, Fast Iterative Method for Solving Toeplitz-plus-Hankel Least Squares Problems, Electronic Transactions on Numerical Analysis, V2 (1994), pp. 154-170
• M. Ng and R. Chan, Fast Iterative Method for Least Squares Estimations, Numerical Algorithms, V6 (1994), pp. 353-378

• X. Lin, M. Ng, H. Sun, Crank-Nicolson ADI Method for Space-Fractional Diffusion Equations with Non-separable Coefficients, SIAM Journal on Numerical Analysis
• X. Lin, M. Ng, H. Sun, Stability and Convergence Analysis of Finite Difference Schemes for Time-Dependent Space-Fractional Diffusion Equations with Variable Diffusion Coefficients, Journal of Scientific Computing, V75 (2018), pp. 1102-1127
• X. Lin, X. Lu, M. Ng, H. Sun, A Fast Accurate Approximation Method with Multigrid Solver for Two-Dimensional Fractional Sub-Diffusion Equation, Journal of Computational Physics, V323 (2016), pp. 204-218
• R. Ke, M. Ng and H. Sun, A Fast Direct Method for Block Triangular Toeplitz-like with Tri-diagonal Block Systems from Time-Fractional Partial Differential Equations, Journal of Computational Physics, V303 (2015), pp. 203-211
• M. Benzi, M. Ng, Q. Niu and Z. Wang, A Relaxed Dimensional Factorization Preconditioner for the Incompressible Navier-Stokes Equations, Journal of Computational Physics, V230 (2011), pp. 6185-6202
• Z. Bai, Y. Huang and M. Ng, On Preconditioned Iterative Methods for Time-dependent Partial Differential Equations, SIAM Journal on Numerical Analysis, V47 (2009), pp. 1019-1037
• Z. Bai, Y. Huang and M. Ng, On Preconditioned Iterative Methods for Burgers Equations, SIAM Journal on Scientific Computing, V29 (2007), pp. 415-439
• Z. Bai, M. Ng and J. Pan, On Alternating Splitting Waveform Relaxation Method and its Successive Overrelaxation Acceleration, Computers and Mathematics with Applications, V49 (2005), pp. 157-170
• J. Pan, Z. Bai and M. Ng, Two-Step Waveform Relaxation Methods for Implicit Linear Initial Value Problems, Numerical Linear Algebra with Applications, V12 (2005), pp. 293-304
• D. Bertaccini, Y. Wen and M. Ng, The Eigenvalues of Preconditioned Matrices for Linear Multistep Formulas in Boundary Value Form, Numerical Linear Algebra with Applications, V12 (2005), pp. 315-325
• D. Bertaccini and M. Ng, Band-Toeplitz Preconditioned GMRES Iterations for Time-dependent PDEs, BIT, V43 (2003), pp. 901-914
• D. Bertaccini and M. Ng, Omega-Circulant Preconditioners for Time-Dependent Integrators, CALCOLO, V40 (2003), pp. 71-90
• W. Cheung and M. Ng, Block-Circulant Preconditioners for Systems Arising from Discretization of the Three-Dimensional Convection-Diffusion Equation, Journal of Computational Applied Math., V140 (2002), pp. 143-158
• R. Chan, M. Ng and X. Jin, Strang's Circulant Preconditioners for Systems of LMF-Based ODE Codes, IMA Journal of Numerical Analysis, V21 (2001), pp. 451-462
• D. Bertaccini and M. Ng, The Convergence Rate of Block Preconditioned Systems Arising from LMF-based ODE Codes, BIT, V41 (2001), pp. 433-450
• F. Lin and M. Ng, Fast Preconditioned Iterative Methods for Convolution-type Integral Equations, BIT, V40 (2000), pp. 336-350
• M. Ng, Preconditioning of Elliptic Problems by Approximation in the Transform Domain, BIT, V37 (1997), pp. 885-900
• F. Lin, M. Ng and R. Chan, Preconditioners for Wiener-Hopf Equations with Higher Order Quadrature Rules, SIAM Journal on Numerical Analysis, V34 (1997), pp. 1418-1431
• M. Ng, Circulant Preconditioners for Convolution-like Integral Equations with Higher Order Quadrature Rules, Electronic Transactions on Numerical Analysis, V5 (1997), pp. 18-28
• M. Ng and F. Lin, Decomposition and Construction of Preconditioners for Wiener-Hopf Equations, SEAMS Math. Bulletin, V20, No. 3 (1996), pp. 71-78
• F. Lin and M. Ng, Higher-order Quadratures for Circulant Preconditioned Wiener-Hopf Equations, BIT, V36, No.1 (1996), pp. 110-121
• R. Chan, X. Jin and M. Ng, Circulant Integral operators as Preconditioners for Wiener-Hopf Equations, Integral Equations and Operator Theory, V21 (1995), pp. 12-23
• M. Ng, F. Lin and R. Chan, Construction of Preconditioners for Wiener-Hopf Equations by Operator Splitting, Applied Mathematics and Computations, V72 (1995), pp. 77-96

• H. Wu, G. Yan, H. Min, M. Ng, Q. Wu, Online Heterogeneous Transfer Learning by Knowledge Transition, ACM Transactions on Intelligent Systems and Technology
• D. Dong, L. Jing, M. Ng, S. Sun and J. Yu, Sentiment Lexicon Construction with Hierarchical Supervision Topic Model, IEEE Transactions on Audio, Speech and Language Processing
• X. Li, M. Ng, X. Xu and Y. Ye, Block Principal Component Analysis for Tensor Objects with Frequency or Time Information, Neurocomputing 302 (2018), pp. 12-22
• H. Tong and M. Ng, Calibration of epsilon-insensitive Loss in Support Vector Machines Regression, Journal of the Franklin Institute
• Y. Yao, X. Li, Y. Ye, F. Liu, M. Ng, Z. Huang, Y. Zhang, Low-resolution Image Categorization via Heterogeneous Domain Adaptation, Knowledge-Based Systems
• H. Zhu, L. Liao and M. Ng, Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality, Neural Computation
• H. Tong and M. Ng, Analysis of Regularized Least Squares for Functional Linear Regression Model, Journal of Complexity
• G. Xu, S. Khan, H. Zhu, L. Han, M. Ng and H. Yan, Discriminative Tracking via Supervised Tensor Learning, Neurocomputing
• S. Jiang, Y. Xu, H. Song, Q. Wu, M. Ng, H. Min, S Qiu, Multi-instance Transfer Metric Learning by Weighted Distribution and Consistent Maximum Likelihood Estimation, Neurocomputing
• C. Chen, J. Xin, Y. Wang, L. Chen and M. Ng, A Semi-supervised Classification Approach for Multi-domain Networks with Domain Selection, IEEE Transactions on Neural Networks and Learning Systems
• H. Tong and M. Ng, Regularized Semi-supervised Least Squares Regression with Dependent Samples, Communications in Mathematical Sciences
• H. Zhu, C. Chen, L. Liao and M. Ng, Multiple Graphs Clustering by Gradient Flow Method, V355 (2018), pp. 1819-1845
• Y. Yan, Q. Wu, M. Tan, M. Ng, H. Min, I. Tsang, Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions, IEEE Transactions on Neural Networks and Learning Systems, V29 (2018), pp. 3252-3263
• X. Li, M. Ng, G. Cong, Y. Ye and Q. Wu, MR-NTD: Manifold Regularization Nonnegative Tucker Decomposition for Tensor Data Dimension Reduction and Representation, IEEE Transactions on Neural Networks and Learning Systems, V28 (2017), pp. 1787-1800
• G. Gan and M. Ng, k-Means Clustering with Outlier Removal, Pattern Recognition Letters, V90 (2017), pp. 8-14
• M. Ng, Q. Wu and C. Shen, A Fast Markov Chain Based Algorithm for MIML Learning, Neurocomputing, V216 (2016), pp. 763-777
• Q. Wu, M. Tan, M. Song, H. Chen and M. Ng, ML-Forest: A Multi-label Tree Ensemble Method for Multi-Label Classification, IEEE Transactions on Knowledge and Data Engineering, V28 (2016), pp. 2665-2680
• L. Yang, L. Jing, J. Yu and M. Ng, Learning Transferred Weights from Co-Occurrence Data for Heterogeneous Transfer Learning, IEEE Transactions on Neural Networks and Learning Systems, V27 (2016), pp. 2187-2200
• G. Gan and M. Ng, Subspace Clustering with Automatic Feature Grouping, Pattern Recognition, V48 (2015), pp. 3703-3713
• D. Chu, L. Liao, M. Ng and X. Wang, Incremental Linear Discriminant Analysis: A New Algorithm and Comparisons, IEEE Transactions on Neural Networks and Learning Systems, V11 (2015), pp. 2716-2735
• G. Gan and M. Ng, Subspace Clustering using Affinity Propagation, Pattern Recognition, V48 (2015), pp. 1455-1464
• Q. Wu, Y. Ye, H. Zhang, M. Ng and S. Ho, ForesTexter: An Efficient Random Forest Algorithm for Imbalanced Text Categorization, Knowledge-Based Systems, V67 (2014), pp. 105-116
• Q. Wu, M. Ng, Y. Ye, X. Li, R. Shi and Y. Li, Multi-Label Collective Classification via Markov Chain Based Learning Method, Knowledge Based System V63 (2014), pp. 1-14
• Q. Wu, M. Ng and Y. Ye, Co-Transfer Learning Using Coupled Markov Chains with Restart, IEEE Intelligent Systems, V29 (2014), pp. 26-33
• D. Chu, L. Liao, M. Ng and X. Zhang, Sparse Canonical Correlation Analysis: New Formulation and Algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence, V35 (2013), pp. 3050-3065
• L. Jing, M. Ng, T. Zeng, Dictionary Learning Based Subspace Structure Identification in Spectral Clustering, IEEE Transactions on Neural Networks and Learning Systems, V24 (2013), pp. 1188-1199
• X. Li, M. Ng, and Y. Ye, MultiComm: Finding Local Community Structure in Multi-dimensional Networks, IEEE Transactions on Knowledge and Data Engineering, V26 (2014), pp. 929-941
• Y. Ye, Q. Wu, J. Huang, M. Ng and X. Li, Stratified Sampling for Feature Subspace Selection in Random Forests for High Dimensional Data, Pattern Recognition, V46 (2013), pp. 769-787
• Q. Wu, M. Ng and Y. Ye, Markov-MIML: A Markov Chain Based Multi-Instance Multi-Label Learning Algorithm, Knowledge and Information Systems, V37 (2013), pp. 83-104
• X. Kong, M. Ng and Z. Zhou, Transductive Multi-Label Learning via Label Set Propagation, IEEE Transactions on Knowledge and Data Engineering, V25 (2013), pp. 704-719.
• X. Li, W. Li, Y. Ye and M. Ng, On Multivariate Markov Chains for Common and Non-common Objects in Multiple Networks, Numerical Mathematics: Theory, Methods and Applications, V5 (2012), pp. 383-402
• Y. Wang, Y. Ye, X. Li, M. Ng and J. Huang, Hierarchical Information-theoretic Co-clustering for High Dimensional Data, International Journal of Innovative Computing, Information and Control, V7 (2011), pp. 487-500
• M. Ng, L. Liao and L. Zhang, On Sparse Linear Discriminant Analysis Algorithm for High-dimensional Data Classification, Numerical Linear Algebra with Applications, V18 (2011), pp. 223-236
• X. Li, Y. Ye, M. Li and M. Ng, On Cluster Tree for Nested and Multi-density Data Clustering, Pattern Recognition, V43 (2010), pp. 3130-3143
• J. Ling, M. Ng and J. Huang, Knowledge-based Vector Space Model for Text Clustering, Knowledge and Information Systems, V25 (2010), pp. 35-55
• J. Ling, M. Li, M. Ng, Y. Cheung and J. Huang, SMART: a Subspace Clustering Algorithm that Automatically Identifies the Appropriate Number of Clusters, International Journal of Data Mining, Modelling and Management, V1 (2009), pp. 149-177
• H. Liao and M. Ng, Categorical Data Clustering with Automatic Selection of Cluster Number, Journal of Fuzzy Information and Engineering, V1 (2009), pp. 5-25
• M. Ng and L. Jing, A New Fuzzy k-Modes Clustering Algorithm for Categorical Data, International Journal of Granular Computing, Rough Sets and Intelligent Systems, V1 (2009), pp. 105-119
• M. Li, M. Ng, Y. Cheung and J. Huang, Agglomerative Fuzzy k-means Algorithm with Selection of Number of Clusters, IEEE Transactions on Knowledge and Data Engineering, V20 (2008), pp. 1519-1534
• X. Zhang, L. Jing, X. Hu, M. Ng, J. Xia and X. Zhou, Medical Document Clustering Using Ontology Based Term Similarity Measures, International Journal of Data Warehousing and Mining, V4 (2008), pp. 62--73
• L. Jing, M. Ng and J. Huang, An Entropy Weighting k-means Algorithm for Subspace Clustering of High-dimensional Sparse Data, IEEE Transactions on Knowledge and Data Engineering, V19 (2007), pp. 1026-1041
• M. Ng, M. Li, J. Huang and Z. He, On the Impact of Dissimilarity Measure in k-modes Clustering Algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence, V29 (2007), pp. 503-507
• M. Ng, E. Chan, M. So and W. Ching, A Semi-Supervised Regression Model for Mixed Numerical and Categorical Variables, Pattern Recognition, V40 (2007), pp. 1745-1752.
• L. Jing, X. Yang, M. Ng, J. Huang, A Text Clustering System based on k-means Type Subspace Clustering and Ontology, International Journal of Intelligent Technology, Vol. 1, No. 2, pp. 91-103.
• V. Torra, J. Domingo-Ferrer, J. Mateo-Sanz and M. Ng, Regression for Ordinal Variables without Underlying Continuous Variables, Information Sciences, V176 (2006), pp. 465-474
• E. Wu, M. Ng and J. Huang, A Data Warehousing and Data Mining Framework for Web Usage Management, Communications in Information and Systems, V4 (2004), pp. 301-324.
• Z. Huang, M. Ng, H. Rong and Z. Li, Automated Variable Weighting in K-Means Type Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, V27 (2005), pp. 657-668
• K. Yip, D. Cheung and M. Ng, HARP: A Highly-usable Projected Clustering Algorithm, IEEE Transactions on Knowledge and Data Engineering, V16 (2004), pp. 1387-1397
• E. Chan, W. Ching, M. Ng and Z. Huang, An Optimization Algorithm for Clustering Using Weighted Dissimilarity Measures Optimization, Pattern Recognition, V37 (2004), pp. 943-952
• Z. Huang and M. Ng, A Note on K-Modes Clustering, Journal of Classification, V20 (2003), pp. 257-261
• Q. Yang, Z. Huang and M. Ng, A Data Cube Model for Prediction-based Web Prefetching, Journal of Intelligent Information Systems, V20 (2003), pp. 11-30
• K. Yip, M. Ng and D. Cheung, A Review on Projected Clustering Algorithms, International Journal of Applied Mathematics, V13 (2003), pp. 24-35
• M. Ng and J. Wong, Clustering Categorical Data Sets Using Tabu Search Techniques, Pattern Recognition, V35 (2002), pp. 2783-2790
• M. Ng, K-means-type Algorithms on Distributed Memory Computer, International Journal of High Speed Computing, V11 (2001), pp. 75-91
• M. Ng, A Note on Constrained K-means Algorithms, Pattern Recognition, V33 (2000), pp. 515-519
• Z. Huang and M. Ng, A Fuzzy K-modes Algorithm for Clustering Categorical Data, IEEE Transactions on Fuzzy Systems, V7 (1999), pp. 446-452
• M. Ng and Z. Huang, Data-Mining Massive Time Series Astronomical Data: Challenges, Problems and Solutions, Information and Software Technology, V41 (1999), pp. 545-556

• Y. Qiu, H. Jiang, W. Ching and M. Ng, On Predicting Epithelial Mesenchymal Transition by Integrating RNA-binding Proteins and Correlation Data via L⁠1/2-regularizatiMn met, Artificial Intelligence In Medicine
• Y. Liu, M. Ng, S. Wu, Multi-domain Networks Association for Biological Data Using Block Signed Graph Clustering, IEEE/ACM Transactions on Computational Biology and Bioinformatics
• L. Zhao, V. Lee, M. Ng, H. Yan and M. Bijlsma, Molecular Subtyping of Cancer: Current Status and Moving Toward Clinical Applications, Briefings in Bioinformatics (2018), pp. 1–13
• C. Chuan, M. Ng and S. Zhang, Block Spectral Clustering for Multiple Graphs with Inter-relation, Network Modeling Analysis in Health Informatics (2017)
• X. Zhang, L. Li, M. Ng and S. Zhang, Drug-target Interaction Prediction by Integrating Multiview Network Data, Computational Biology and Chemistry V69 (2017), pp. 185-193
• S. Zhang and M. Ng, Gene-microRNA Network Module Analysis for Ovarian Cancer, BMC System Biology, Volume 10 Supplement 4 (2016)
• C. Shen, J. Pan, S. Zhang and M. Ng, Multiple Networks Modules Identification by a Multi-dimensional Markov Chain Method, Network Modeling Analysis in Health Informatics and Bioinformatics, 4:32 (2015)
• S. Zhang, H. Zhao and M. Ng, Functional Module Analysis for Gene Coexpression Networks with Network Integration, IEEE/ACM Transactions on Computational Biology and Bioinformatics, V12 (2015), pp. 1146-1160
• H. Cai, P. Ruan, M. Ng and T. Akutsu, Feature Weight Estimation for Gene Selection: a Local Hyper-Linear Learning Spproach, BMC Bioinformatics, 15:70 (2014)
• Q. Wu, Y. Ye, M. Ng, S. Ho and R. Shi, Collective Prediction of Protein Functions from Protein-Protein Interaction Networks, BMC Bioinformatics, 15:S9 (2014)
• X. Li, Y. Ye, M. Ng and Q. Wu, MultiFacTV: Module Detection from Higher-order Time Series Biological Data, BMC Genomics 14:S2 (2013)
• H. Pang, T. Tong and M. Ng, Block-diagonal Discriminant Analysis and Its Bias-corrected Rules, Statistical Applications in Genetics and Molecular Biology, V12 (2013), pp. 347-359
• Y. Liu, X. Li, Z. Liu, L. Chen and M. Ng, Construction and Analysis of SNP-SNP Interaction Networks, IET Systems Biology, V7 (2013), pp. 170-181
• Q. Wu, Y. Ye, Y. Liu and M. Ng, Stratified Random Forest for Genome-wide Association Study, IEEE Transactions on NanoBioScience, V11 (2012), pp. 216-227
• Y. Liu and M. Ng, Classification of Genome-wide Copy Number Variations and their Associated SNP and Gene Networks Analysis, BMC Bioinformatics, 12:S4 (2011)
• L. Jing, T. Zeng and M. Ng, On Gene Selection and Classification for Cancer Microarray Data Using Multi-step Clustering and Sparse Representation, Advances in Adaptive Data Analysis, V3 (2011), pp. 127-148
• L. Jing and M. Ng, Prior Knowledge Based Mining Functional Modules from Yeast PPI Networks with Gene Ontology, BMC Bioinformatics, 11:S3 (2010)
• Y. Liu and M. Ng, Shrunken Methodology to Genome-wide SNPs Selection and Construction of SNPs Networks, BMC Systems Biology, 4:S7, (2010)
• Y. Liu, M. Li, Y. Cheung, P. Sham and M. Ng, SKM-SNP: SNP Markers Detection Method, Journal of Biomedical Informatics, V43 (2010), pp. 233-239
• L. Jing, M. Ng and Y. Liu, Construction of Gene Networks with Hybrid Approach from Expression Profile and Gene Ontology, IEEE Transactions on Information Technology in Biomedicine, V14 (2010), pp. 107-118
• S. Zhang, H. Wang, X. Zhou, G. Hoehn, T. DeGraba, D. Gonzales, A. Suffredini, W. Ching, M. Ng and S. Wong, A Novel Peak Detection Approach with Chemical Noise Removal Using Short-Time FFT for prOTOF MS Data, Proteomics, V9 (2009), pp. 3833-3842
• X. Hu, M. Ng, F. Wu and B. Sokhansanj, Mining, Modeling and Evaluation of Sub-Networks from Large Biomolecular Networks and its Comparison Study, IEEE Transactions on Information Technology in Biomedicine, V13 (2009), pp. 184-194
• T. Akutsu, M. Hayashida, S. Zhang, W. Ching and M. Ng, Analyses and Algorithms for Predecessor and Control Problems for Boolean Networks of Bounded Indegree, IPSJ Transactions on Bioinformatics, V1 (2008), pp. 23-34
• K. Yip, L. Cheung, D. Cheung, L. Jing and M. Ng, A Semi-supervised Approach to Projected Clustering with Applications to Microarray Data, International Journal of Data Mining and Bioinformatics, V3 (2009), pp. 229-259
• E. Fung and M. Ng, On Sparse Fisher Discriminant Method for Microarray Data Analysis, Bioinformation, V2 (2007), pp. 222-229
• H. Liao, M. Ng, E. Fung and P. Sham, Unidimensional Nonnegative Scaling for Genome-wide Linkage Disequilibrium Maps, International Journal of Bioinformatics Research and Applications, V4 (2008), pp. 417-434
• L. Cheung, Y. Lee, T. Ng, W. Ching, U. Khoo, M. Ng and A. Wong, CpG/CpNpG Motifs in the Coding Region are Preferred Sites for Mutagenesis in the Breast Cancer Susceptibility Genes, FEBS Letters, V581 (2007), pp. 4668-4674
• W. Ching, S. Zhang, M. Ng and T. Akutsu, An Approximation Method for Solving the Steady-state Probability Distribution of Probabilistic Boolean Networks, Bioinformatics, V23 (2007), pp. 1511-1518
• S. Zhang, M. Hayashida, T. Akutsu, W. Ching and M. Ng, Algorithms for Finding Small Attractors in Boolean Networks, EURASIP Journal on Bioinformatics and Systems Biology, V2007 (2007), 20180
• P. Sham, S. Ao, P. Koo, P. Yee and M. Ng, Combining Functional and Linkage Disequilibrium Information in the Selection of Tag SNPs, Bioinformatics V23 (2007), pp. 129-131
• T. Akutsu, M. Hayashida, W. Ching and M. Ng, Control of Boolean Networks: Hardness Results and Algorithms for Tree Structured Networks, Journal of Theoretical Biology, V244 (2007), pp. 670-679
• A. Yip, M. Ng, E. Wu and T. Chan, Strategies for Identifying Statistically Significant Dense Regions in Microarray Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, V4(3) (2007), pp. 415-429
• S. Ao, M. Ng and P. Sham, Constrained Unidimensional Scaling with Applications to Genomics, Computational Statistics and Data Analysis, V52 (2007), pp. 201-210
• S. Zhang, W. Ching, M. Ng and T. Akutsu, Simulation Study in Probabilistic Boolean Network Models for Genetic Regulatory Networks, International Journal of Data Mining and Bioinformatics, V1 (2007), pp. 217-240
• M. Ng, E. Fung, Y. Li and W. Ching, A Recursive Method for Solving Haplotype Frequencies with Application to Genetics, Journal of Bioinformatics and Computational Biology, V4 (2007) pp. 1269-1286
• C. Lin, K. Yip, D. Cheung, B. Kao and M. Ng, On Mining Micro-array Data by OPSM, International Journal of Bioinformatics Research and Applications, V3 (2007), pp. 42-64.
• S. Ao and M. Ng, Gene Expression Time Series Modeling with Principal Component and Neural Network, Soft Computing, V10 (2006), pp. 351-358
• W. Ching, M. Ng, E. Fung and T. Akutsu, On Construction of Stochastic Genetic Networks Based on Gene Expression Sequences, International Journal of Neural Systems, 15 (2005) pp. 297-310
• S. Ao, K. Yip, M. Ng, D. Cheung, P. Yee, I. Melhado and P. Sham, CLUSTAG: Hierarchical Clustering and Graph Methods for Selecting Tag SNPs, Bioinformatics, V21 (2005) pp. 1735-1736
• K. Yip, D. Cheung, M. Ng and K. Cheung, Identifying Projected Clusters from Gene Expression Profiles, Journal of Biomedical Informatics, V37 (2004) pp. 345-357

• M. Ng and Y. Chung, Double Mover-stayer Model on Customer Switching in Telecommunication Industry, Naval Research Logistics, V59 (2012) pp. 663-674
• G. Fusai, D. Marazzina, M. Marena and M. Ng, Z-transform and Preconditioning Techniques for Option Pricing, Quantitative Finance, V12 (2012), pp. 1381-1394
• T. Siu, E. Fung and M. Ng, Option Valuation with a Discrete-Time Double Markovian Regime-Switching Model, Applied Mathematical Finance, V18 (2011), pp. 473-490
• T. Siu, W. Ching, E. Fung, M. Ng and X. Li, A Higher-order Markov-switching Model for Risk Measurement, Computers and Mathematics Applications, 58 (2009), pp. 1-10
• W. Ching, E. Fung, M. Ng, T. Siu and W. Li, Interactive Hidden Markov Models and Their Applications, IMA Journal of Management Mathematics, V18 (2007), 85-97
• K. Siu, M. Ng and E. Fung, On Neural Network Conditional Autoregressive Value at Risk, Mathematical Modelling and Applied Computing
• W. Ching, S. Zhang and M. Ng, On Multi-dimensional Markov Chain Models, Pacific Journal of Optimization, V3 (2007), 235-243
• T. Siu, W. Ching. E. Fung and M. Ng, Extracting Information from Spot Interest Rates and Credit Ratings using Double Higher-Order Hidden Markov Models, Journal of Computational Economics 26 (2005) 251-284
• W. Ching, W. Yuen, M. Ng, and S. Zhang A Linear Programming Approach for Determining Optimal Advertising Policy, IMA Journal of Management Mathematics, V17 (2006) pp. 83-96
• T. Siu, W. Ching, E. Fung and M. Ng, On Multivariate Credibility Approach for Portfolio Credit Risk Measurement, Quantitative Finance, V5 (2005), pp. 543-556
• W. Ching, E. Fung and M. Ng, Building Higher-order Markov Chain Models with EXCEL, International Journal of Mathematical Education in Science and Engineering, V35 (2004), pp. 921-932
• W. Ching, M. Ng, K. Wong and E. Altman, Customer Lifetime Value: Stochastic Optimization Approach, Journal of Operational Research Society, V55 (2004), pp. 860-868
• W. Ching, M. Ng and M. So, Customer Migration, Campaign Budgeting, Revenue Estimation: the Elasity of Markov Decision Process on Customer Lifetime Value, Electronic International Journal of Advanced Modeling and Optimization, V6(2) (2004), pp. 65-80
• W. Ching, M. Ng, and K. Wong, Hidden Markov Models and its Applications to Customer Relationship Management, IMA Journal on Management Mathematics, V15 (2004), pp. 13-24
• W. Ching and M. Ng, Building Simple Hidden Markov Model, International Journal of Mathematical Education in Science and Technology, V35 (2004), pp. 295-299
• W. Ching, E. Fung and M. Ng, Higher-order Markov Chain Models for Categorical Data Sequences, Naval Research Logistics, V51 (2004), pp. 557-574
• W. Ching, E. Fung and M. Ng, Higher-order Markov Model for the Newsboy's Problem, Journal of Operational Research Society, V54 (2003), pp. 291-298
• W. Ching, E. Fung and M. Ng, A Multivariate Markov Chain Model for Categorical Data Sequences and Its Applications in Demand Predictions, IMA Journal on Management Mathematics, V13 (2002) 187-199
• M. Ng, Clustering Methods for Printed Circuit Board Insertion Problems, Journal of Operational Research Society, V51 (2000), pp. 1205-1211
• M. Ng, A Note on Policy Algorithms for Discounted Markov Decision Problems, Operations Research Letters, V25 (1999), pp. 195-197
• M. Ng, Heuristics Approach to Printed Circuit Board Insertion Problem, Journal of Operational Research Society, V49 (1998), pp. 1051-1059

• Y. Sun, M. Ng, Z. Zhou: Multi-Instance Dimensionality Reduction. AAAI 2010

• R. Chan and M. Ng, Preconditioning Toeplitz Systems with Circulant Preconditioners, Iterative Methods in Scientific Computing, Springer-Verlag, 1997, pp. 243-270
• R. Chan and M. Ng, Iterative Methods for Linear Systems with Matrix Structure, Fast Reliable Algorithms for Matrices with Structure, SIAM, 1999, pp. 132-174
• R. Chan, M. Ng and X. Jin, Circulant Preconditioners for Solving Ordinary Differential Equations, Structured Matrices: Recent Developments in Theory and Computation. Nova Science Pub. Inc., 2000, pp. 157-164
• W. Kwan and M. Ng, Iterative Regularization for Ill-Posed Imaging Problems Using Neumann Boundary Conditions, Structured Matrices: Recent Developments in Theory and Computation, Nova Science Pub. Inc., 2000, pp. 177-190
• R. Chan, M. Ng and A. Yip, A Survey of Preconditioners for Ill-Conditioned Toeplitz Systems, Structured Matrices in Mathematics, Computer Science, and Engineering II, Contemporary Mathematics, AMS, 2001, pp. 175-191
• M. Ng, N. Bose and J. Koo, Constrained Total Least Squares for Color Image Reconstruction, Total Least Squares and Errors-in-Variables Modelling III: Analysis, Algorithms and Applications, Kluer Academic Publishers, 2002, pp. 365-374
• M. Ng and R. Plemmons, Blind Deconvolution and Structured Matrix Computations with Applications to Array Imaging, Blind Image Deconvolution: Theory and Applications, CRC Press, pp. 377-418
• M. Ng, Structured Matrix Computations, Handbook of Linear Algebra, CRC Press, CRC Press, Chapter 48
• E. Wu and M. Ng, Developing a Knowledge-Based Intelligent Services System in Sports Websites, E-Service Intelligence---Methodologies, Technologies and Applications, Springer, Chapter 25, pp. 535-554