Name
Abstract | ||
|---|---|---|
•The proposed low rank based dictionary learning method can captures the global structure of data and handle complex noise.•We learn an adaptive dictionary so that it can better characterize complex structured noise.•Our proposed optimization method can converge to a critical point and the convergence rate is at least sublinear. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2017.07.041 | Neurocomputing |
Keywords | Field | DocType |
Dictionary learning,Structured noise,Low rank representation,Sparse representation | K-SVD,Pattern recognition,Noise measurement,Computer science,Sparse approximation,Robustness (computer science),Gaussian,Artificial intelligence,Norm (mathematics),Cluster analysis,Gaussian noise,Machine learning | Journal |
Volume | ISSN | Citations |
273 | 0925-2312 | 4 |
PageRank | References | Authors |
0.42 | 33 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhou, P. | 1 | 88 | 11.59 |
| Cong Fang | 2 | 17 | 7.14 |
| Zhouchen Lin | 3 | 4805 | 203.69 |
| Chao Zhang | 4 | 27 | 3.64 |
| Edward Y. Chang | 5 | 4519 | 336.59 |