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.
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SNP Selection and Classification of Genome-Wide SNP Data Using Stratified Sampling Random Forests
TL;DR: The idea is to design an equal-width discretization scheme for informativeness to divide SNPs into multiple groups and randomly select the same number of SNPs from each group and combine them to form a subspace to generate a decision tree.
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Computing Moore-Penrose inverses of Toeplitz matrices by Newton's iteration
TL;DR: This work modifications the algorithm of [1], based on Newton's iteration and on the concept of @e-displacement rank, to the computation of the Moore-Penrose inverse of a rank-deficient Toeplitz matrix.
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Development and validation of prognosis model of mortality risk in patients with COVID-19.
Xuedi Ma,Michael K. Ng,Shuang Xu,Zhouming Xu,Hui Qiu,Yuwei Liu,Jiayou Lyu,Jiwen You,Peng Zhao,Shihao Wang,Yunfei Tang,Hao Cui,Changxiao Yu,Feng Wang,Fei Shao,Peng Sun,Ziren Tang +16 more
TL;DR: LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission and this model was better than CURB-65, AUROC and the machine-learning-based model.
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Deep plug-and-play prior for low-rank tensor completion
TL;DR: A novel regularized tensor completion model for multi-dimensional image completion that can recover both the global structure and fine details very well and achieve superior performance over competing methods in terms of quality metrics and visual effects is suggested.
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Nonconvex-TV Based Image Restoration with Impulse Noise Removal
TL;DR: The proposed image restoration model can be solved by the proximal linearized minimization algorithm, and the global convergence of the iterative algorithm can also be established according to Kurdyka--Łojasiewicz property.