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Non-negative_matrix_factorization abstract "Non-negative matrix factorization (NMF), also non-negative matrix approximation is a group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into (usually) two matrices W and H, with the property that all three matrices have no negative elements. This non-negativity makes the resulting matrices easier to inspect. Also, in applications such as processing of audio spectrograms non-negativity is inherent to the data being considered. Since the problem is not exactly solvable in general, it is commonly approximated numerically.NMF finds applications in such fields as computer vision, document clustering, chemometrics, audio signal processing and recommender systems.".
Non-negative_matrix_factorization thumbnail NMF.png?width=300.
Non-negative_matrix_factorization wikiPageExternalLink 785152.abs.html.
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Non-negative_matrix_factorization wikiPageWikiLinkText "NMF".
Non-negative_matrix_factorization wikiPageWikiLinkText "Non-negative Matrix Factorization".
Non-negative_matrix_factorization wikiPageWikiLinkText "Non-negative matrix factorization (NMF)".
Non-negative_matrix_factorization wikiPageWikiLinkText "Non-negative matrix factorization".
Non-negative_matrix_factorization wikiPageWikiLinkText "Non-negative_matrix_factorization".
Non-negative_matrix_factorization wikiPageWikiLinkText "non-negative matrix factorization".
Non-negative_matrix_factorization wikiPageWikiLinkText "non-negative matrix/tensor factorization".
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