Paper Info
Title
Low complexity approximate cyclic adaptive matching pursuit.
Abstract
Based on the iterated cyclic adaptive matching pursuit algorithm, we construct a low complexity approximate variant for finding sparse solutions to systems of linear equations. We employ a greedy neighbor permutation strategy coupled with an approximate scalar product matrix to ensure that the complexity of the algorithm remains low. The sparse solution is cyclically updated improving the performance while the sparsity level is estimated online using the predictive least squares criterion. The performance of the algorithm is similar to that of the non approximate variants while the complexity can be considerably lower.
Year
Venue
Keywords
2012
European Signal Processing Conference
matching pursuit,sparse filters,greedy algorithm,channel identification
DocType
ISSN
Citations 
Conference
2076-1465
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Alexandru Onose1123.93
Bogdan Dumitrescu210722.76