Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better empirical performance. However, the resulting optimization problem is...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 41(2019), 11 vom: 24. Nov., Seite 2628-2643 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article en ligne |
| Langue: | English |
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2019
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article |
| Accès en ligne |
Volltext |