Michael K. Ng

631 papers receiving 19.8k citations

Michael K. Ng's Hit Papers

An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data 2007 · 450 citations
4500+10+20Years since publication250500750

Peers

Michael K. Ng
Comparison fields: 5 of 205
  • Computational Mathematics 1.8k
  • Numerical Analysis 2.1k
  • Computer Vision and Pattern Recognition 8.0k
  • Media Technology 2.8k
  • Computational Theory and Mathematics 4.3k
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Countries citing papers authored by Michael K. Ng

Since Specialization
Citations

This map shows the geographic impact of Michael K. Ng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael K. Ng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael K. Ng more than expected).

Fields of papers citing papers by Michael K. Ng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael K. Ng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael K. Ng. The network helps show where Michael K. Ng may publish in the future.

Co-authors

The 25 scholars most cited alongside Michael K. Ng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael K. Ng Line = papers co-authored together Michael K. Ng links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 668 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Hermitian and Skew-Hermitian Splitting Methods for Non-Hermitian Positive Definite Linear Systems
Hit paper breakdown →
2003797
2
Conjugate Gradient Methods for Toeplitz Systems
Hit paper breakdown →
1996602
3
Automated variable weighting in k-means type clustering
Hit paper breakdown →
2005568
4 2006451
5
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
Hit paper breakdown →
2007450
6 1999343
7 2005336
8 1999331
9 2009249
10 2009243
11 2004238
12 2011217
13 2008207
14 2004202
15 2006201
16 2010197
17 2010189
18 2008173
19 2004169
20 1982168

About Michael K. Ng

Michael K. Ng is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Artificial Intelligence, Computational Theory and Mathematics and Computational Mathematics, having authored 668 papers that have together received 20.7k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (153 papers), Image and Signal Denoising Methods (146 papers), Matrix Theory and Algorithms (114 papers), Advanced Image Processing Techniques (74 papers), Tensor decomposition and applications (74 papers), Electromagnetic Scattering and Analysis (58 papers), Numerical methods in inverse problems (50 papers) and Advanced Image Fusion Techniques (44 papers). The work is most often cited by research in Computational Mathematics (1.8k citations), Numerical Analysis (2.1k citations), Computer Vision and Pattern Recognition (8.0k citations), Media Technology (2.8k citations) and Computational Theory and Mathematics (4.3k citations). Michael K. Ng has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Zhong‐Zhi Bai, Gene H. Golub, Wai‐Ki Ching, Raymond H. Chan, Mila Nikolova, Liping Jing, Joshua Zhexue Huang, Yumei Huang, You‐Wei Wen and Xi-Le Zhao. Their work appears in journals such as SIAM Journal on Scientific Computing, SIAM Journal on Imaging Sciences, Numerical Linear Algebra with Applications, IEEE Transactions on Image Processing and SIAM Journal on Matrix Analysis and Applications.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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