Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
564478 | Signal Processing | 2010 | 5 Pages |
Abstract
Sparse approximation in a redundant basis has attracted considerable attention in recent years because of many practical applications. The problem basically involves solving an under-determined system of linear equations under some sparsity constraint. In this paper, we present a simple interpretation of the recently proposed complementary matching pursuit (CMP) algorithm. The interpretation shows that the CMP, unlike the classical MP, selects an atom and determines its weight based on a certain sparsity measure of the resulting residual error. Based on this interpretation, we also derive another simple algorithm which is seen to outperform CMP at low sparsity levels for noisy measurement vectors.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Gagan Rath, Christine Guillemot,