Low-rank Approximation
Low-Rank Approximation is a matrix approximation technique in numerical linear algebra that replaces a given matrix with a lower-rank matrix to minimize approximation error, typically by capturing dominant singular components while discarding less significant ones.
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2017–2026
1.6K
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Robust principal component analysis?
Emmanuel J. Candès, Xiaodong Li, Yi Ma et al. · Journal of the ACM · 2011 · 6.9K citations
The Approximation of One Matrix by Another of Lower Rank
Carl Eckart, Gale Young · Psychometrika · 1936 · 3.8K citations
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University of Electronic Science and Technology of China, China
27 publications · 1.7K citations
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University of Electronic Science and Technology of China, China
22 publications · 1.5K citations
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Stanford University
Stanford, United States
41 h-index
152 publications
41.7K citations

University of Electronic Science and Technology of China
Chengdu, China
40 h-index
251 publications
14.2K citations

Chinese Academy of Sciences
Beijing, China
39 h-index
232 publications
13.1K citations
Massachusetts Institute of Technology
Cambridge, United States
38 h-index
138 publications
17.1K citations

Xidian University
Xi'an, China
35 h-index
162 publications
9.3K citations
The University of Texas at Austin
Austin, United States
34 h-index
131 publications
11.1K citations
National University of Singapore
Singapore, Singapore
34 h-index
106 publications
15.8K citations
University of Illinois Urbana-Champaign
Urbana, United States
34 h-index
106 publications
28.1K citations
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Repository · 284 publications · 13.6K citations
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127 publications · 11.1K citations
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91 publications · 7.5K citations
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99 publications · 7.4K citations
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76 publications · 7.9K citations
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