Cyclic adaptive matching pursuit


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.

DOI: 10.1109/ICASSP.2012.6288731

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@article{Onose2012CyclicAM, title={Cyclic adaptive matching pursuit}, author={Alexandru Onose and Bogdan Dumitrescu}, journal={2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2012}, pages={3745-3748} }