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
Birkhoff--von Neumann Theorem for Multistochastic Tensors
Lu-Bin Cui,Wen Li,Michael K. Ng +2 more
TL;DR: The Birkhoff--von Neumann theorem is studied and it is found that extreme points in the set of multistochastic tensors are not just permutation tensors.
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Clinical manifestation and neonatal outcomes of pregnant patients with coronavirus disease 2019 pneumonia in Wuhan, China
Shuang Xu,Fei Shao,Banghe Bao,Xuedi Ma,Zhouming Xu,Jiwen You,Peng Zhao,Yuwei Liu,Michael K. Ng,Hao Cui,Changxiao Yu,Qing Zhang,Dandan Li,Ziren Tang,Peng Sun +14 more
TL;DR: Clinical characteristics of pregnant patients with CO VID-19 are less serious than nonpregnant and no evidence indicated that pregnant women may have fetal infection through vertical transmission of COVID-19.
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Low Rank Tensor Completion with Poisson Observations.
Xiongjun Zhang,Michael K. Ng +1 more
TL;DR: Wang et al. as mentioned in this paper considered the maximum likelihood estimate of the Poisson distribution, and utilized the Kullback-Leibler divergence for the data-fitting term to measure the observations and the underlying tensor.
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A Semisupervised Segmentation Model for Collections of Images
TL;DR: A semisupervised optimization model that determines an efficient segmentation of many input images that is highly controllable by the user so that the user can easily specify what he/she wants.
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Efficient box-constrained TV-type-l1 algorithms for restoring images with impulse noise
TL;DR: Numerical algorithms based on the derivation of exact total variation and the use of proximal operators are developed that are efficient in computational time and effective in restoring images with impulse noise.