A fast approach for overcomplete sparse decomposition based on smoothed L0 norm
Abstract
In this paper, a fast algorithm for overcomplete sparse decomposition, called SL0, is proposed. The algorithm is essentially a method for obtaining sparse solutions of underdetermined systems of linear equations, and its applications include underdetermined Sparse Component Analysis (SCA), atomic decomposition on overcomplete dictionaries, compressed sensing, and decoding real field codes. Contrary to previous methods, which usually solve this problem by minimizing the L1 norm using Linear Programming (LP) techniques, our algorithm tries to directly minimize the L0 norm. It is experimentally shown that the proposed algorithm is about two to three orders of magnitude faster than the state-of-the-art interior-point LP solvers, while providing the same (or better) accuracy.
Cite
@article{arxiv.0809.2508,
title = {A fast approach for overcomplete sparse decomposition based on smoothed L0 norm},
author = {Hossein Mohimani and Massoud Babaie-Zadeh and Christian Jutten},
journal= {arXiv preprint arXiv:0809.2508},
year = {2009}
}
Comments
Accepted in IEEE Transactions on Signal Processing. For MATLAB codes, see (http://ee.sharif.ir/~SLzero). File replaced, because Fig. 5 was missing erroneously