Name
Abstract | ||
|---|---|---|
The recovery of sparse signals in underdetermined systems is the focus of this paper. We propose an expanded version of the Variational Garrote, originally presented by Kappen (2011), which can use multiple measurement vectors (MMVs) to further improve source retrieval performance. We show its superiority compared to the original formulation and demonstrate its ability to correctly estimate both the sources' location and their magnitude. Finally evidence is given of the high performance of the proposed algorithm compared to other MMV models. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.3233/978-1-61499-330-8-105 | Frontiers in Artificial Intelligence and Applications |
Keywords | DocType | Volume |
Variational Garrote,sparsity,Bayesian inference,temporally correlated sources,multiple measurements vector (MMV) | Conference | 257 |
ISSN | Citations | PageRank |
0922-6389 | 2 | 0.41 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sofie Therese Hansen | 1 | 9 | 3.93 |
| Carsten Stahlhut | 2 | 55 | 8.47 |
| Lars Kai Hansen | 3 | 2776 | 341.03 |