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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Vydané v:	IEEE transactions on image processing Ročník 29; s. 2244 - 2257
Hlavní autori:	Huang, Yan, Liao, Guisheng, Xiang, Yijian, Zhang, Lei, Li, Jie, Nehorai, Arye
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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