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
Minimax Current Density Coil Design
TL;DR: In this paper, the authors proposed three different base functions (triangular elements with uniform current, cylindrical elements with sinusoidal current and conic section elements with conusoidal-uniform current) for gradient and shim coils.
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Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix Approximation
TL;DR: It is shown that the sequence generated by the alternating projections onto the tangent spaces of the fixed rank matrices manifold and the nonnegative matrix manifold, converge linearly to a point in the intersection of the two manifolds where the convergent point is sufficiently close to optimal solutions.
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On Recovery of Sparse Signals With Prior Support Information via Weighted ℓ ₚ -Minimization
TL;DR: In this paper, Wang et al. established a complete characterization for the restricted isometry constant (RIC) bounds on the sparse signal recovery with prior support information via weighted ''ell ''p'' minimization.
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High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization
TL;DR: A uniformly dithered one-bit quantization scheme for highdimensional statistical estimation that achieves optimal minimax rates up to logarithmic factors and a novel setting where both measurement and covariate are quantized is first proposed and studied.
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On Multivariate Markov Chains for Common and Non-Common Objects in Multiple Networks
TL;DR: The main contribution of this paper is to model multiple networks where there are some common objects in a multivariate Markov chain framework, and to develop a method for solving common and non-common ob- jects' stationary probability distributions in the networks.