Name
Title | ||
|---|---|---|
Multi-dimensional sparse structured signal approximation using split bregman iterations |
Abstract | ||
|---|---|---|
The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlying optimization problem is tackled using a multi-dimensional split Bregman optimization approach. An extensive empirical evaluation shows how the proposed approach compares to the state of the art depending on the signal features. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638374 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
approximation theory,iterative methods,optimisation,signal representation,multidimensional sparse structured signal approximation,multidimensional split Bregman optimization approach,overcomplete signal representations,signal features,split Bregman iteration approach,Fused-LASSO,Multidimensional signals,Regularization,Sparse approximation,Split Bregman | Multi dimensional,Mathematical optimization,Pattern recognition,Iterative method,Computer science,Sparse approximation,Approximation theory,Regularization (mathematics),Artificial intelligence,Optimization problem | Conference |
Volume | ISSN | Citations |
abs/1303.5197 | 1520-6149 | 1 |
PageRank | References | Authors |
0.35 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Isaac | 1 | 1 | 0.35 |
| Q. Barthelemy | 2 | 1 | 0.35 |
| Jamal Atif | 3 | 309 | 29.49 |
| Cédric Gouy-Pailler | 4 | 62 | 10.69 |