Name
Abstract | ||
|---|---|---|
The computational cost of many signal processing and machine learning techniques is often dominated by the cost of applying certain linear operators to high-dimensional vectors. This paper introduces an algorithm aimed at reducing the complexity of applying linear operators in high dimension by approximately factorizing the corresponding matrix into few sparse factors. The approach relies on recen... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/JSTSP.2016.2543461 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | DocType | Volume |
Sparse matrices,Dictionaries,Complexity theory,Transforms,Inverse problems,Approximation algorithms,Optimization | Journal | 10 |
Issue | ISSN | Citations |
4 | 1932-4553 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luc Le Magoarou | 1 | 14 | 5.02 |
| Rémi Gribonval | 2 | 1207 | 83.59 |