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...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence. - 1979. - 41(2019), 11 vom: 24. Nov., Seite 2628-2643
1. Verfasser:	Yao, Quanming (VerfasserIn)
Weitere Verfasser:	Kwok, James T, Wang, Taifeng, Liu, Tie-Yan
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2019
Zugriff auf das übergeordnete Werk:	IEEE transactions on pattern analysis and machine intelligence
Schlagworte:	Journal Article