Name
Abstract | ||
|---|---|---|
This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first update the dictionary using the method of optimal directions (MOD) and then apply a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition of ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1162/NECO_a_00907 | Neural Computation |
Field | DocType | Volume |
Least squares,Dictionary learning,Shrinkage,K-SVD,Coherence (physics),Artificial intelligence,Coordinate descent,Mutual coherence,Machine learning,Mathematics | Journal | 29 |
Issue | ISSN | Citations |
1 | 0899-7667 | 1 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| shashanka ubaru | 1 | 58 | 8.97 |
| Abd-Krim Seghouane | 2 | 78 | 12.27 |
| Yousef Saad | 3 | 1940 | 254.74 |