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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Book ChapterDOI
On Computation with Higher-order Markov Chains
TL;DR: This paper proposes a higher-order Markov chain model for modeling categorical data sequences where the number of model parameters increases linearly with respect to the order of the model.
Proceedings ArticleDOI
Tensor Based Relations Ranking for Multi-relational Collective Classification
TL;DR: An algorithm, TensorRRCC, is proposed, able to determine the ranking of relations and the labels of objects simultaneously, which is compared with current collective classification algorithms on two real-world data sets and the experimental results show the superiority of the method.
Journal ArticleDOI
Preconditioned Iterative Methods for Algebraic Systems from Multiplicative Half-Quadratic Regularization Image Restorations
TL;DR: This paper considers a class of convex and edge-preserving regularization functions, i.e., multiplicative half-quadratic regularizations, and uses the Newton method to solve the correspondingly reduced systems of nonlinear equations.
Posted Content
Nonnegative Low Rank Tensor Approximation and its Application to Multi-dimensional Images
TL;DR: Experimental results for synthetic data and multi-dimensional images are presented to demonstrate the performance of the proposed NLRT method is better than that of existing NTF methods.
Proceedings ArticleDOI
Cross-track Illumination Correction For Hyperspectral Pushbroom Sensors Using Total Variation and Sparsity Regularization
Lina Zhuang,Michael K. Ng +1 more
TL;DR: A column (along-track) mean compensation approach with total variation and sparsity regularization (COMCO-TVS) is proposed, which corrects the illumination via exploiting characteristics of column-mean pixels and column- mean illumination errors: piecewise smoothness andSparsity, respectively, in the spatial-spectral domain.