Name
Title | ||
|---|---|---|
Sparse Representations For Multiple Measurement Vectors (Mmv) In An Over-Complete Dictionary |
Abstract | ||
|---|---|---|
Multiple Measurement Vector (MMV) is a newly emerged problem in sparse representation in an over-complete dictionary; it poses new challenges. Efficient methods have been designed to search for sparse representations [1, 2]; however, we have not seen substantial development in the theoretical analysis, considering, what has been done in a simpler case-Single Measurement Vector (SMV)-in which many theoretical results are known, e.g., [9, 3, 4, 5, 6]. This paper extends the known results of SMV to MMV.Our theoretical results show the fundamental limitation on when a sparse representation is unique. Moreover, the relation between the solutions of l(0)-norm minimization and the solutions of l(1)-norm minimization indicates a computationally efficient approach to find a sparse representation. Interestingly, simulations show that the predictions made by these theorems tend to be conservative. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICASSP.2005.1415994 | 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING |
Keywords | Field | DocType |
design methodology,minimisation,sparse matrices,inverse problems,inverse problem,magnetoencephalography,dictionaries,vectors,sparse representation,computational modeling,biomedical imaging,meg,predictive models | Mathematical optimization,K-SVD,Computer science,Medical imaging,Sparse approximation,Theoretical computer science,Minimisation (psychology),Minification,Inverse problem,Sparse matrix,Matching pursuit algorithms | Conference |
ISSN | Citations | PageRank |
1520-6149 | 16 | 5.01 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Chen | 1 | 2487 | 353.65 |
| Xiaoming Huo | 2 | 157 | 24.83 |