Name
Abstract | ||
|---|---|---|
•Real world images can be modeled by a union of subspaces and sparse outliers.•Nonconvex optimization problems can be used to learn the UoS and sparse models.•Linearized ADMM methods are highly efficient in solving nonconvex optimizations.•The performance of RoSuRe can be verified both theoretically and experimentally. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.sigpro.2018.05.024 | Signal Processing |
Keywords | Field | DocType |
Signal recovery,Sparse learning,Subspace modeling | Clustering high-dimensional data,Subspace topology,Computer science,Sparse approximation,Linear subspace,Theoretical computer science,Sparse matrix | Journal |
Volume | ISSN | Citations |
152 | 0165-1684 | 0 |
PageRank | References | Authors |
0.34 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Bian | 1 | 11 | 4.26 |
| Ashkan Panahi | 2 | 93 | 13.97 |
| Hamid Krim | 3 | 520 | 59.69 |