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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Total variation structured total least squares method for image restoration
TL;DR: An efficient alternating minimization scheme is developed to solve the proposed model by minimizing two variables: the restored image and the estimated error of the point spread function.
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Higher-order Markov chain models for categorical data sequences*
TL;DR: This paper applies the developed higher‐order Markov chain model for analyzing categorical data sequences to the server logs data to model the users' behavior in accessing information and to predict their behavior in the future.
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Constrained total least‐squares computations for high‐resolution image reconstruction with multisensors
TL;DR: A regularized constrained total least‐squares (RCTLS) solution to the problem is given, which requires the minimization of a nonconvex and nonlinear cost functional and simulations indicate that the choice of the regularization parameter influences significantly the quality of the solution.
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Efficient Total Variation Minimization Methods for Color Image Restoration
TL;DR: The convergence of the alternating minimization algorithm is shown and it is demonstrated that the algorithm is very efficient and the quality of restored color images by the proposed method are competitive with the other tested methods.
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ML-FOREST: A Multi-Label Tree Ensemble Method for Multi-Label Classification
TL;DR: This paper proposes a new algorithm, called Ml-Forest, to learn an ensemble of hierarchical multi-label classifier trees to reveal the intrinsic label dependencies, and develops a label transfer mechanism to identify the multiple relevant labels in a hierarchical way.