Low-rank Optimization
Low-Rank Optimization is a variational optimization method that restricts the search space to matrices of bounded or minimized rank, thereby exploiting low-rank structure to reduce complexity and enhance generalization in high-dimensional problems.
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149.9K
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4.7K
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Publications per year
2017–2026
1.1K
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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
Robust Subspace Segmentation by Low-Rank Representation
Guangcan Liu, Zhouchen Lin, Yong Yu · 2010 · 1.4K citations
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University of Electronic Science and Technology of China, China
22 publications · 1.6K citations
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University of Electronic Science and Technology of China
Chengdu, China
36 h-index
183 publications
14.1K citations

Chinese Academy of Sciences
Beijing, China
35 h-index
169 publications
13.9K citations
National University of Singapore
Singapore, Singapore
33 h-index
95 publications
15.6K citations

Xi'an Jiaotong University
Xi'an, China
29 h-index
97 publications
7.3K citations

Peking University
Beijing, China
28 h-index
85 publications
6.7K citations
Nanjing University of Science and Technology
Nanjing, China
28 h-index
131 publications
7.4K citations
Stanford University
Stanford, United States
28 h-index
82 publications
23.4K citations

Xidian University
Xi'an, China
27 h-index
109 publications
7.2K citations
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83 publications · 8K citations
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Repository · 171 publications · 9.6K citations
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58 publications · 4.2K citations
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58 publications · 4.7K citations
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42 publications · 3K citations
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