H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 68 Citations 15,442 355 World Ranking 924 National Ranking 78
Mathematics D-index 57 Citations 14,517 304 World Ranking 305 National Ranking 10

Research.com Recognitions

Awards & Achievements

2017 - SIAM Fellow For fundamental contributions to algorithms for structured linear systems and image processing.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

His scientific interests lie mostly in Artificial intelligence, Algorithm, Pattern recognition, Mathematical optimization and Image restoration. Michael K. Ng has researched Artificial intelligence in several fields, including Linear subspace and Computer vision. Michael K. Ng combines subjects such as Markov decision process, Attractor and Categorical variable with his study of Algorithm.

His Pattern recognition research includes elements of Weighting, Simple random sample and Random forest. His Mathematical optimization research is multidisciplinary, incorporating elements of Image, Total variation denoising, Applied mathematics and Convex optimization. He interconnects Regularization, Noise reduction, Numerical linear algebra and Iterative reconstruction in the investigation of issues within Image restoration.

His most cited work include:

  • Hermitian and Skew-Hermitian Splitting Methods for Non-Hermitian Positive Definite Linear Systems (739 citations)
  • Conjugate Gradient Methods for Toeplitz Systems (660 citations)
  • Automated variable weighting in k-means type clustering (539 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Algorithm, Applied mathematics, Pattern recognition and Data mining. Michael K. Ng works mostly in the field of Artificial intelligence, limiting it down to topics relating to Markov chain and, in certain cases, Markov process, as a part of the same area of interest. His Algorithm study incorporates themes from Image processing, Mathematical optimization and Iterative reconstruction.

The concepts of his Applied mathematics study are interwoven with issues in Linear system, Preconditioner, Matrix, Toeplitz matrix and Numerical analysis. His Toeplitz matrix research integrates issues from Circulant matrix, Hermitian matrix, Mathematical analysis and Conjugate gradient method. His Data mining study frequently draws connections to adjacent fields such as Cluster analysis.

He most often published in these fields:

  • Artificial intelligence (27.23%)
  • Algorithm (21.88%)
  • Applied mathematics (15.07%)

What were the highlights of his more recent work (between 2018-2021)?

  • Applied mathematics (15.07%)
  • Algorithm (21.88%)
  • Rank (5.19%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Applied mathematics, Algorithm, Rank, Tensor and Artificial intelligence. His Applied mathematics research is multidisciplinary, relying on both Linear system, Preconditioner, Matrix, Discretization and Numerical analysis. His Algorithm research includes themes of Norm and Hyperspectral imaging.

His Rank research focuses on Tensor and how it connects with Upper and lower bounds. The study incorporates disciplines such as Matrix decomposition, Order and Markov chain in addition to Tensor. In Artificial intelligence, Michael K. Ng works on issues like Pattern recognition, which are connected to Subspace topology.

Between 2018 and 2021, his most popular works were:

  • 3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model (30 citations)
  • Robust quaternion matrix completion with applications to image inpainting (22 citations)
  • Molecular subtyping of cancer: current status and moving toward clinical applications. (18 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

Michael K. Ng mostly deals with Artificial intelligence, Applied mathematics, Tensor, Singular value decomposition and Rank. His Artificial intelligence research incorporates themes from Efficient algorithm, Simple, Computer vision and Pattern recognition. His work deals with themes such as Image colorization and Noise reduction, which intersect with Pattern recognition.

He has included themes like Circulant matrix, Linear system, Discretization, Focus and Conditional probability distribution in his Applied mathematics study. His research on Tensor also deals with topics like

  • Matrix norm that connect with fields like Convex relaxation, Plug and play and Artificial neural network,
  • Tensor which connect with Feature, Tucker decomposition, Feature vector, Cluster analysis and Data modeling,
  • Matrix decomposition which connect with Social network, Scale, Recommendation quality and Recommender system. His research integrates issues of Matrix and Quaternion in his study of Singular value decomposition.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Conjugate Gradient Methods for Toeplitz Systems

Raymond H. Chan;Michael K. Ng.
Siam Review (1996)

955 Citations

Hermitian and Skew-Hermitian Splitting Methods for Non-Hermitian Positive Definite Linear Systems

Zhong-Zhi Bai;Gene H. Golub;Michael K. Ng.
SIAM Journal on Matrix Analysis and Applications (2002)

907 Citations

Automated variable weighting in k-means type clustering

J.Z. Huang;M.K. Ng;Hongqiang Rong;Zichen Li.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)

893 Citations

An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data

Liping Jing;M.K. Ng;J.Z. Huang.
IEEE Transactions on Knowledge and Data Engineering (2007)

723 Citations

A fuzzy k-modes algorithm for clustering categorical data

Zhexue Huang;M.K. Ng.
IEEE Transactions on Fuzzy Systems (1999)

577 Citations

A Fast Algorithm for Deblurring Models with Neumann Boundary Conditions

Michael K. Ng;Raymond H. Chan;Wun-Cheung Tang.
SIAM Journal on Scientific Computing (1999)

536 Citations

Control of Boolean networks: hardness results and algorithms for tree structured networks.

Tatsuya Akutsu;Morihiro Hayashida;Wai-Ki Ching;Michael K. Ng.
Journal of Theoretical Biology (2007)

458 Citations

Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery

Mila Nikolova;Michael K. Ng.
SIAM Journal on Scientific Computing (2005)

386 Citations

Iterative Methods for Toeplitz Systems

Michael K Ng.
(2004)

332 Citations

Markov Chains: Models, Algorithms and Applications

Wai-Ki Ching;Ximin Huang;Michael K. Ng;Tak Kuen Siu.
(2006)

330 Citations

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