Sparse Signal Representation
Sparse Signal Representation is a signal modeling framework in which a signal is expressed as a linear combination of a few selected basis elements from an overcomplete dictionary, thereby emphasizing parsimony in the representation through minimal non-zero coefficients.
5.3K
Publications
444.4K
Citations
10.6K
Authors
2.2K
Institutions
Publications per year
2017–2026
1.6K
Parents
5.3K
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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10.6K
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32
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Institut national de recherche en sciences et technologies du numérique, France
46 publications · 4.2K citations
30
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29
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25
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2.2K
Technion – Israel Institute of Technology
Haifa, Israel
54 h-index
228 publications
64.1K citations
Stanford University
Stanford, United States
48 h-index
147 publications
84.3K citations

Chinese Academy of Sciences
Beijing, China
44 h-index
226 publications
14.9K citations
Tsinghua University
Beijing, China
44 h-index
292 publications
14.3K citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
43 h-index
195 publications
12.7K citations

Xidian University
Xi'an, China
42 h-index
409 publications
21.7K citations

Centre National de la Recherche Scientifique
Paris, France
41 h-index
196 publications
20.3K citations

Rice University
Houston, United States
40 h-index
149 publications
27.5K citations
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336 publications · 64.8K citations
100
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134 publications · 34.7K citations
68
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104 publications · 9.6K citations
54
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141 publications · 10.1K citations
50
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116 publications · 16K citations
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