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
A multivariate Markov chain model for categorical data sequences and its applications in demand predictions
Book ChapterDOI
A comparative study of ontology based term similarity measures on PubMed document clustering
TL;DR: This paper evaluates term re-weighting as an important method to integrate domain ontology to clustering process and results on 8 different semantic measures show there is no a certain type of similarity measures that significantly outperforms the others.
Journal ArticleDOI
Multiplicative Noise Removal via a Learned Dictionary
TL;DR: This paper proposes to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal, suggesting that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods.
Journal ArticleDOI
An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks
TL;DR: An approximation method for computing the steady-state probability distribution of a PBN based on neglecting some Boolean networks (BNs) with very small probabilities during the construction of the transition probability matrix is proposed.
Journal ArticleDOI
CLUSTAG: hierarchical clustering and graph methods for selecting tag SNPs
Sio Iong Ao,Kevin Y. Yip,Michael K. Ng,David W. Cheung,Pui-Yee Fong,Ian G. Melhado,Pak C. Sham +6 more
TL;DR: Cluster and set-cover algorithms are developed to obtain a set of tag single nucleotide polymorphisms (SNPs) that can represent all the known SNPs in a chromosomal region, subject to the constraint that all SNPs must have a squared correlation R2>C with at least one tag SNP, where C is specified by the user.