Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6941412 | Signal Processing: Image Communication | 2018 | 33 Pages |
Abstract
Matrix completion is a challenging problem with a range of real applications. Many existing methods are based on low-rank prior of the underlying matrix. However, this prior may not be sufficient to recover the original matrix from its incomplete observations. In this paper, we propose a novel matrix completion algorithm by employing the low-rank prior and a sparse prior simultaneously. Specifically, the matrix completion task is formulated as a rank minimization problem with a sparse regularizer. The low-rank property is modeled by the truncated nuclear norm to approximate the rank of the matrix, and the sparse regularizer is formulated as an â1-norm term based on a given transform operator. To address the raised 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 show the effectiveness of the proposed algorithm and its better performance as compared with the state-of-the-art matrix completion algorithms.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Jing Dong, Zhichao Xue, Jian Guan, Zi-Fa Han, Wenwu Wang,