Name
Abstract | ||
|---|---|---|
The multiple measurement vector problem (MMV) is a generalization of the compressed sensing problem that addresses the recovery of a set of jointly sparse signal vectors. One of the important contributions of this paper is to reveal that the seemingly least related state-of-art MMV joint sparse recovery algorithms - M-SBL (multiple sparse Bayesian learning) and subspace-based hybrid greedy algorithms - have a very important link. More specifically, we show that replacing the log det(·) term in M-SBL by a log det(·) rank proxy that exploits the spark reduction property discovered in subspace-based joint sparse recovery algorithms, provides significant improvements. Theoretical analysis demonstrates that even thoughM-SBL is often unable to remove all localminimizers, the proposed method can do so under fairly mild conditions, without affecting the global minimizer. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638824 | ICASSP |
Keywords | Field | DocType |
multiple measurement vector problem,joint sparse recovery algorithms,sparse signal vectors,bayes methods,learning (artificial intelligence),subspace penalized sparse learning,compressed sensing problem,compresse sensing,compressed sensing,greedy algorithms,joint sparse recovery,mmv,spark reduction property,multiple sparse bayesian learning,subspace-based hybrid greedy algorithms,m-sbl,vectors,signal to noise ratio,learning artificial intelligence,multiple signal classification,algorithm design and analysis,sensors | Mathematical optimization,Spark (mathematics),Bayesian inference,Subspace topology,Pattern recognition,Computer science,Sparse approximation,Greedy algorithm,Artificial intelligence,Compressed sensing,Sparse learning | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jong Chul Ye | 1 | 715 | 79.99 |
| Jongmin Kim | 2 | 32 | 5.39 |
| Yoram Bresler | 3 | 1104 | 119.17 |