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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Hermitian and Skew-Hermitian Splitting Methods for Non-Hermitian Positive Definite Linear Systems
TL;DR: In this paper, the authors studied efficient iterative methods for the large sparse non-Hermitian positive definite system of linear equations based on the Hermitian and skew-hermitian splitting of the coefficient matrix.
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Conjugate Gradient Methods for Toeplitz Systems
Raymond H. Chan,Michael K. Ng +1 more
TL;DR: Some of the latest developments in using preconditioned conjugate gradient methods for solving Toeplitz systems are surveyed, finding that the complexity of solving a large class of $n-by-n$ ToePlitz systems is reduced to $O(n \log n)$ operations.
Hermitian and Skew-Hermitian splitting methods for non-Hermitian positive definite linear systems.
TL;DR: An upper bound of the contraction factor of the HSS iteration is derived which is dependent solely on the spectrum of the Hermitian part and is independent of the eigenvectors of the matrices involved.
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Automated variable weighting in k-means type clustering
TL;DR: A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed, and the convergency theorem of the new clustered process is given.
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An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
TL;DR: This paper presents a new k-means type algorithm for clustering high-dimensional objects in sub-spaces that can generate better clustering results than other subspace clustering algorithms and is also scalable to large data sets.