Name
Abstract | ||
|---|---|---|
We present an improved Adaptive Matching Pursuit algorithm for computing approximate sparse solutions for overdetermined systems of equations. The algorithms use a greedy approach, based on a neighbor permutation, to select the ordered support positions followed by a cyclical optimization of the selected coefficients. The sparsity level of the solution is estimated on-line using Information Theoretic Criteria. The performance of the algorithm approaches that of the sparsity informed RLS, while the complexity remains lower than that of competing methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICASSP.2012.6288731 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
approximation theory,channel allocation,computational complexity,greedy algorithms,iterative methods,optimisation,approximate sparse solutions computing,competing methods,cyclic adaptive matching pursuit algorithm,cyclical optimization,greedy approach,information theoretic criteria,neighbor permutation,online estimation,overdetermined systems of equations,sparsity informed RLS,adaptive algorithm,channel identification,matching pursuit,sparse filters | Matching pursuit,Approximation algorithm,Overdetermined system,Mathematical optimization,Pattern recognition,Computer science,Iterative method,Permutation,Greedy algorithm,Artificial intelligence,Adaptive algorithm,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4673-0044-5 | 978-1-4673-0044-5 | 2 |
PageRank | References | Authors |
0.47 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandru Onose | 1 | 12 | 3.93 |
| Bogdan Dumitrescu | 2 | 107 | 22.76 |