Low rank matrix completion using truncated nuclear norm and sparse regularizer

作者：

Highlights：

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• This paper proposes a novel matrix completion algorithm by employing a low-rank prior based on truncated nuclear norm and a sparse prior simultaneously.

• To address the resulting optimization problem, a method alternating between two steps is developed, and the problem involved in the second step is converted to several subproblems with closed-form solutions.

• Experimental results demonstrate the effectiveness of the proposed algorithm and its better performance as compared with the state-of-the-art matrix completion algorithms.

摘要

•This paper proposes a novel matrix completion algorithm by employing a low-rank prior based on truncated nuclear norm and a sparse prior simultaneously.•To address the resulting optimization problem, a method alternating between two steps is developed, and the problem involved in the second step is converted to several subproblems with closed-form solutions.•Experimental results demonstrate the effectiveness of the proposed algorithm and its better performance as compared with the state-of-the-art matrix completion algorithms.

论文关键词：Matrix completion,Low rank,Truncated nuclear norm,Sparse representation

论文评审过程：Received 31 October 2017, Revised 15 June 2018, Accepted 18 June 2018, Available online 11 July 2018, Version of Record 24 July 2018.

论文官网地址：https://doi.org/10.1016/j.image.2018.06.007