Name
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 ℓ1-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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.image.2018.06.007 | Signal Processing: Image Communication |
Keywords | Field | DocType |
Matrix completion,Low rank,Truncated nuclear norm,Sparse representation | Matrix completion,Matrix (mathematics),Computer science,Algorithm,Theoretical computer science,Matrix norm,Low-rank approximation,Operator (computer programming),Rank minimization,Optimization problem | Journal |
Volume | ISSN | Citations |
68 | 0923-5965 | 1 |
PageRank | References | Authors |
0.37 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Dong | 1 | 6 | 3.19 |
| Zhichao Xue | 2 | 1 | 0.70 |
| Guan Jian | 3 | 8 | 3.69 |
| Zifa Han | 4 | 13 | 4.22 |
| Wenwu Wang | 5 | 333 | 52.60 |