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
Book ChapterDOI
Queueing Systems and the Web
TL;DR: This chapter presents an important numerical algorithm based on the computation of Markov chains for ranking webpages and discusses some more Markovian queueing systems.
Proceedings ArticleDOI
Restoration of images with optical aberrations and quantization in a transform domain
Edmund Y. Lam,Michael K. Ng +1 more
TL;DR: A restoration algorithm that handles images with optical aberrations and quantization in a transform domain is presented and it is proved analytically that the algorithm is globally convergent to a unique solution when the restoration uses either H1-norm or TV-norm regularization.
Proceedings ArticleDOI
Approximate Secular Equations for the Cubic Regularization Subproblem
TL;DR: A novel CRS solver based on an approximate secular equation, which requires only some of the Hessian eigenvalues and is therefore much more efficient, which makes it particularly suitable for high-dimensional applications of unconstrained non-convex optimization, such as low-rank recovery and deep learning.
Proceedings ArticleDOI
Parallel image processing algorithms for coincidence Doppler broadening spectra
TL;DR: The paper reports the performance of the parallel image deconvolution algorithm on the IBM SP2 computer and the generalized least-square method with Tikhonov-Miller regularization is developed by incorporating a priori information of non-negativity into the mathematical regularization technique for the solution of blurring matrix equations.
Journal ArticleDOI
Sparse Nonnegative Tucker Decomposition and Completion under Noisy Observations
Xiongjun Zhang,Michael K. Ng +1 more
TL;DR: Numerical examples on both synthetic and real-world data sets demonstrate the superiority of the proposed method for nonnegative tensor data completion, better than existing tensor-based or matrix-based methods.