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
Galerkin Projection Methods for Solving Multiple Linear Systems
Tony F. Chan,Michael K. Ng +1 more
TL;DR: A theoretical error bound is given for the approximation obtained from a projection process onto a Krylov subspace generated from solving a previous linear system and numerical results for multiple linear systems arising from image restorations and recursive least squares computations are reported to illustrate the effectiveness of the method.
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Phase Retrieval from Incomplete Magnitude Information via Total Variation Regularization
TL;DR: This paper addresses the phase retrieval problem from incomplete data, and considers structured illuminated patterns in holography and finds that noninteger values used in designing such patterns often yield better reconstruction than the conventional integer-valued ones.
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Tensor-Based Low-Dimensional Representation Learning for Multi-View Clustering
TL;DR: A tensor-based representation learning method for multi-view clustering (tRLMvC) that can unify heterogeneous and high-dimensional multi- view feature spaces to a low-dimensional shared latent feature space and improve multi-View clustering performance is introduced.
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Circulant and skew-circulant splitting methods for Toeplitz systems
TL;DR: An upper bound of the contraction factor of the CSCS iteration which is dependent solely on the spectra of the circulant and the skew-circulant matrices involved is derived.
Posted Content
Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment
TL;DR: The proposed Tensor-train, High-order pooling and Semisupervised learning-based GAN (THS-GAN) is the first work to deal with classification on MR images for AD diagnosis and achieves superior performance compared with existing methods.