Sparse Coding
Sparse Coding is a representation learning method in unsupervised learning that characterizes a data point as a sparse linear combination of basis elements from an overcomplete dictionary, where the sparsity constraint distinguishes it from other factorization or encoding techniques.
3K
Publications
278.5K
Citations
6.7K
Authors
1.6K
Institutions
Publications per year
2017–2026
979
3K
Robust Face Recognition via Sparse Representation
John Wright, A. Yang, Arvind Ganesh et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2009 · 9.5K citations · Full text
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University of Illinois Urbana-Champaign, United States
25 publications · 16.3K citations
23
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21
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1–5 of 6.7K
1.6K

Chinese Academy of Sciences
Beijing, China
41 h-index
199 publications
14.4K citations

Xidian University
Xi'an, China
39 h-index
285 publications
24K citations
National University of Singapore
Singapore, Singapore
39 h-index
122 publications
15.4K citations
University of Illinois Urbana-Champaign
Urbana, United States
38 h-index
122 publications
77.2K citations
Technion – Israel Institute of Technology
Haifa, Israel
37 h-index
132 publications
55.8K citations

Harbin Institute of Technology
Harbin, China
35 h-index
161 publications
12.4K citations
Tsinghua University
Beijing, China
31 h-index
198 publications
12K citations
Stanford University
Stanford, United States
31 h-index
86 publications
24.6K citations
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121 publications · 32.5K citations
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85 publications · 22.7K citations
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Repository · 107 publications · 4.4K citations
35
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50 publications · 4K citations
35
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96 publications · 3.4K citations
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