Name
Abstract | ||
|---|---|---|
Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the LASSO cost function based on the cross-products between the reconstruction coefficients. Although the resulting optimization problem is non-convex, we develop a new and efficient method for solving it based on successive convex approximations. Results on synthetic data, for both complete and overcomplete dictionaries, are provided to validate the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638840 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
approximation theory,concave programming,convex programming,learning (artificial intelligence),signal reconstruction,cross-product LASSO cost function,dictionary,negative cooccurrence phenomenon,nonconvex optimization problem,signal processing application,signal reconstruction,sparse learning approach,successive convex approximation,LASSO,negative co-occurrence,sparse coding,sparsity-aware learning | Signal processing,Mathematical optimization,Pattern recognition,Computer science,Lasso (statistics),Sparse approximation,Approximation theory,Synthetic data,Artificial intelligence,Convex optimization,Optimization problem,Signal reconstruction | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Luengo | 1 | 2 | 1.45 |
| Javier Vía | 2 | 412 | 40.39 |
| Sandra Monzón | 3 | 4 | 1.58 |
| Tom Trigano | 4 | 7 | 4.27 |
| Antonio Artés-Rodríguez | 5 | 206 | 34.76 |