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
More filters
Proceedings ArticleDOI
Construction and analysis of genome-wide SNP networks
TL;DR: A novel method to mine, model and evaluate a SNP sub-network from SNP-SNP interactions, based on logistic regression between two SNPs is presented and results show that the method is effective in SNPSub-network extraction and gene function prediction.
Journal ArticleDOI
Construction of preconditioners for Wiener-Hopf equations by operator splitting
TL;DR: It is proved that the spectra of the resulting preconditioned operators ( 1 u )∑ v (αI+P τ (u,v) ) −1 are clustered around 1 and thus the algorithm converges sufficiently fast, and the methods converges faster than those preconditionsed by using circulant integral operators.
Proceedings ArticleDOI
A hybrid algorithm for spatial and wavelet domain image restoration
TL;DR: The recent algorithm ForWaRD based on the two steps: the Fourier domain deblurring and wavelet domain denoising shows better restoration results than those using traditional image restoration methods.
Journal ArticleDOI
Hypergraph Collaborative Network on Vertices and Hyperedges
TL;DR: This paper proposes a novel model called Hypergraph Collaborative Network (HCoN), which takes the information from both previous vertices and hyperedges into consideration to achieve informative latent representations and further introduces the hypergraph reconstruction error as a regularizer to learn an effective classifier.
Journal ArticleDOI
Superresolution image reconstruction from blurred observations by multisensors
TL;DR: This article presents a joint minimization model with an objective function setup that comprises three terms: the data‐fitting term (DFT), the regularization term for the reconstructed image, and the observed low‐resolution images.