Name
Abstract | ||
|---|---|---|
Given a dataset, the task of learning a transform that allows sparse representations of the data bears the name of dictionary learning. In many applications, these learned dictionaries represent the data much better than the static well-known transforms (Fourier, Hadamard etc.). The main downside of learned transforms is that they lack structure and, therefore, they are not computationally efficie... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TSP.2017.2712120 | IEEE Transactions on Signal Processing |
Keywords | DocType | Volume |
Dictionaries,Transforms,Computational complexity,Optimization,Signal processing algorithms,Learning systems | Journal | 65 |
Issue | ISSN | Citations |
16 | 1053-587X | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristian Rusu | 1 | 399 | 45.44 |
| John S. Thompson | 2 | 32 | 11.59 |