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
相关论文
共 50 条
  • [1] Weighted truncated nuclear norm regularization for low-rank quaternion matrix completion
    Yang, Liqiao
    Kou, Kit Ian
    Miao, Jifei
    [J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2021, 81
  • [2] Low-Rank Tensor Completion by Truncated Nuclear Norm Regularization
    Xue, Shengke
    Qiu, Wenyuan
    Liu, Fan
    Jin, Xinyu
    [J]. 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2018, : 2600 - 2605
  • [3] Matrix Completion by Truncated Nuclear Norm Regularization
    Zhang, Debing
    Hu, Yao
    Ye, Jieping
    Li, Xuelong
    He, Xiaofei
    [J]. 2012 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2012, : 2192 - 2199
  • [4] Nuclear Norm Minus Frobenius Norm Minimization with Rank Residual Constraint for Image Denoising
    Huang, Hua
    Shan, Yiwen
    Li, Chuan
    Wang, Zhi
    [J]. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2024, E107D (08) : 992 - 1006
  • [5] HIGH DYNAMIC RANGE IMAGING VIA TRUNCATED NUCLEAR NORM MINIMIZATION OF LOW-RANK MATRIX
    Lee, Chul
    Lam, Edmund Y.
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS, 2016, : 1229 - 1233
  • [6] Nuclear norm regularization with a low-rank constraint for matrix completion
    Zhang, Hui
    Cheng, Lizhi
    Zhu, Wei
    [J]. INVERSE PROBLEMS, 2010, 26 (11)
  • [7] A reweighted nuclear norm minimization algorithm for low rank matrix recovery
    Li, Yu-Fan
    Zhang, Yan-Jiao
    Huang, Zheng-Hai
    [J]. JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2014, 263 : 338 - 350
  • [8] A robust low-rank matrix completion based on truncated nuclear norm and Lp-norm
    Liang, Hao
    Kang, Li
    Huang, Jianjun
    [J]. JOURNAL OF SUPERCOMPUTING, 2022, 78 (11): : 12950 - 12972
  • [9] A robust low-rank matrix completion based on truncated nuclear norm and Lp-norm
    Hao Liang
    Li Kang
    Jianjun Huang
    [J]. The Journal of Supercomputing, 2022, 78 : 12950 - 12972
  • [10] Weighted hybrid truncated norm regularization method for low-rank matrix completion
    Xiying Wan
    Guanghui Cheng
    [J]. Numerical Algorithms, 2023, 94 : 619 - 641