Singular Value Decomposition
Singular Value Decomposition is a matrix factorization method that decomposes a real or complex matrix into the product of three matrices—where the first and third are unitary (or orthogonal in the real case) and the second is a diagonal matrix with non-negative real numbers on the diagonal—thereby revealing the canonical structure of linear transformations in terms of singular values and associated singular vectors.
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Indexing by latent semantic analysis
Scott Deerwester, Susan Dumais, George W. Furnas et al. · Journal of the American Society for Information Science · 1990 · 12.8K citations
Tensor Decompositions and Applications
Tamara G. Kolda, Brett W. Bader · SIAM Review · 2009 · 10.5K citations
Missing value estimation methods for DNA microarrays
Olga G. Troyanskaya, Michael Cantor, Gavin Sherlock et al. · Bioinformatics · 2001 · 4.3K citations · Full text
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Istituto per il Rilevamento Elettromagnetico dell'Ambiente, Italy
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Stanford University
Stanford, United States
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148 publications
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Centre National de la Recherche Scientifique
Paris, France
36 h-index
280 publications
24.8K citations
Massachusetts Institute of Technology
Cambridge, United States
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121 publications
15K citations
University of California, Berkeley
Berkeley, United States
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85 publications
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University of Electronic Science and Technology of China
Chengdu, China
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131 publications
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Chinese Academy of Sciences
Beijing, China
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201 publications
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KU Leuven
Leuven, Belgium
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158 publications
8.8K citations
Carnegie Mellon University
Pittsburgh, United States
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75 publications
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Repository · 117 publications · 6.2K citations
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