Name
Title | ||
|---|---|---|
Exact Low-Rank Matrix Completion from Sparsely Corrupted Entries Via Adaptive Outlier Pursuit. |
Abstract | ||
|---|---|---|
Recovering a low-rank matrix from some of its linear measurements is a popular problem in many areas of science and engineering. One special case of it is the matrix completion problem, where we need to reconstruct a low-rank matrix from incomplete samples of its entries. A lot of efficient algorithms have been proposed to solve this problem and they perform well when Gaussian noise with a small variance is added to the given data. But they can not deal with the sparse random-valued noise in the measurements. In this paper, we propose a robust method for recovering the low-rank matrix with adaptive outlier pursuit when part of the measurements are damaged by outliers. This method will detect the positions where the data is completely ruined and recover the matrix using correct measurements. Numerical experiments show the accuracy of noise detection and high performance of matrix completion for our algorithms compared with other algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10915-013-9682-3 | J. Sci. Comput. |
Keywords | Field | DocType |
algorithms,gaussian noise,measurement | Mathematical optimization,Matrix completion,Matrix (mathematics),Outlier,Low-rank approximation,Grassmannian,Gaussian noise,Sparse matrix,Mathematics,Special case | Journal |
Volume | Issue | ISSN |
56 | 3 | 1573-7691 |
Citations | PageRank | References |
9 | 0.61 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Yan | 1 | 269 | 17.69 |
| Yi Yang | 2 | 92 | 9.96 |
| Stanley Osher | 3 | 7973 | 514.62 |