LOW RANK MATRIX MINIMIZATION WITH A TRUNCATED DIFFERENCE OF NUCLEAR NORM AND FROBENIUS NORM REGULARIZATION

被引:4
|
作者
Guo, Huiyuan [1 ]
Yu, Quan [1 ]
Zhang, Xinzhen [1 ]
Cheng, Lulu [1 ]
机构
[1] Tianjin Univ, Sch Math, Tianjin 300350, Peoples R China
关键词
Low rank matrix minimization; forward-backward splitting; REWEIGHTED LEAST-SQUARES; ALGORITHM; RECOVERY;
D O I
10.3934/jimo.2022045
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this paper, we present a novel regularization with a truncated difference of nuclear norm and Frobenius norm of form L-t,L-*-alpha F with an integer t and parameter a for rank minimization problem. The forward-backward splitting (FBS) algorithm is proposed to solve such a regularization problem, whose subproblems are shown to have closed-form solutions. We show that any accumulation point of the sequence generated by the FBS algorithm is a first-order stationary point. In the end, the numerical results demonstrate that the proposed FBS algorithm outperforms the existing methods.
引用
收藏
页码:2354 / 2366
页数:13
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