Low-Rank Approximation via Generalized Reweighted Iterative Nuclear and Frobenius Norms

The low-rank approximation problem has recently attracted wide concern due to its excellent performance in realworld applications such as image restoration, traffic monitoring, and face recognition. Compared with the classic nuclear norm, the Schatten-p norm is stated to be a closer approximation to...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 29; S. 2244 - 2257
Hauptverfasser:	Huang, Yan, Liao, Guisheng, Xiang, Yijian, Zhang, Lei, Li, Jie, Nehorai, Arye
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation Approximation algorithms Face recognition generalized iterative reweighted Frobenius norm (GIRFN) generalized iterative reweighted nuclear norm (GIRNN) Image decomposition Image restoration Iterative algorithms Iterative methods Low-rank approximation problem matrix completion (MC) Matrix decomposition Matrix methods Minimization Norms Object recognition Optimization Principal component analysis Principal components analysis robust principal component analysis (RPCA) Robustness (mathematics) Sparse matrices
ISSN:	1057-7149, 1941-0042, 1941-0042
Online-Zugang:	Volltext
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