Truncated Cauchy Non-Negative Matrix Factorization
Non-negative matrix factorization (NMF) minimizes the euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated CauchyNMF loss that handle outliers b...
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Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 41(2019), 1 vom: 28. Jan., Seite 246-259
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| 1. Verfasser: |
Guan, Naiyang
(VerfasserIn) |
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| Weitere Verfasser: |
Liu, Tongliang,
Zhang, Yangmuzi,
Tao, Dacheng,
Davis, Larry S |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2019
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article
Research Support, Non-U.S. Gov't |
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