Title :
Cyclic adaptive matching pursuit
Author :
Onose, Alexandru ; Dumitrescu, Bogdan
Author_Institution :
Dept. of Signal Process., Tampere Univ. of Technol., Tampere, Finland
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.
Keywords :
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; Adaptation models; Approximation algorithms; Complexity theory; Estimation; Matching pursuit algorithms; Signal processing algorithms; Vectors; adaptive algorithm; channel identification; matching pursuit; sparse filters;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4673-0045-2
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2012.6288731