Sparse Parameter Estimation
Sparse Parameter Estimation is a parameter estimation method that specifically seeks to identify a minimal subset of nonzero parameters within an underdetermined or noisy system by enforcing sparsity-promoting constraints or priors, distinguishing it from general estimation techniques through its focus on parsimonious model representation.
661
Publications
55.3K
Citations
1.4K
Authors
584
Institutions
Publications per year
2017–2026
235
661
1–5 of 661
1.4K
Massachusetts Institute of Technology, United States
16 publications · 2.1K citations
16
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12
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10
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9
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7
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1–5 of 1.4K
584
Stanford University
Stanford, United States
14 h-index
32 publications
22.9K citations
University of California San Diego
San Diego, United States
14 h-index
27 publications
2K citations
Massachusetts Institute of Technology
Cambridge, United States
14 h-index
27 publications
4.1K citations

Uppsala University
Uppsala, Sweden
13 h-index
24 publications
3.3K citations
Princeton University
Princeton, United States
12 h-index
27 publications
2.1K citations

Centre National de la Recherche Scientifique
Paris, France
11 h-index
21 publications
494 citations
Tsinghua University
Beijing, China
10 h-index
19 publications
719 citations
University of Florida
Gainesville, United States
10 h-index
13 publications
1.3K citations
1–8 of 584
40 publications · 3.5K citations
26
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29 publications · 3.9K citations
26
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Repository · 57 publications · 2.3K citations
26
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19 publications · 808 citations
14
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CSDA · 14 publications · 418 citations
12
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