M
Michael K. Ng
Researcher at University of Hong Kong
Publications - 658
Citations - 24376
Michael K. Ng is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Cluster analysis & Computer science. The author has an hindex of 72, co-authored 608 publications receiving 20492 citations. Previous affiliations of Michael K. Ng include The Chinese University of Hong Kong & Vanderbilt University.
Papers
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Journal ArticleDOI
On mining micro-array data by Order-Preserving Submatrix
TL;DR: This work devise a novel algorithm for mining OPSM, show that OPSM can be generalised to cover most existing pattern-based clustering models and propose a number of extensions to the original OPSM model.
Book ChapterDOI
Skew-Circulant Preconditioners for Systems of LMF-Based ODE Codes
Daniele Bertaccini,Michael K. Ng +1 more
TL;DR: A nonsingular skew-circulant preconditioner for systems of LMF-based ODE codes is proposed and Numerical results are given to illustrate the effectiveness of this method.
Journal ArticleDOI
A Nonlocal Total Variation Model for Image Decomposition: Illumination and Reflectance
Wei Wang,Michael K. Ng +1 more
TL;DR: Zhang et al. as discussed by the authors used nonlocal bounded variation (NLBV) techniques to decompose an image intensity into the illumination and reflectance components by considering spatial smoothness of the illumination component and nonlocal total variation (NLTV) of the reflectance component in the decomposition framework.
Journal ArticleDOI
A Fast Algorithm for Deconvolution and Poisson Noise Removal
TL;DR: An alternating minimization algorithm for Poisson noise removal with nonnegative constraint is proposed that minimizes the sum of a Kullback-Leibler divergence term and a total variation term by utilizing the quadratic penalty function technique.
Journal ArticleDOI
On adaptively accelerated Arnoldi method for computing PageRank
TL;DR: Numerical results show that the proposed Arnoldi method converges faster than existing methods, in particular when the damping factor is large and the weights adaptively are changed based on the current residual corresponding to the approximate PageRank vector.