Efficient Least Residual Greedy Algorithms for Sparse Recovery
Numerical Analysis
2020-12-02 v1 Information Theory
Numerical Analysis
math.IT
Abstract
We present a novel stagewise strategy for improving greedy algorithms for sparse recovery. We demonstrate its efficiency both for synthesis and analysis sparse priors, where in both cases we demonstrate its computational efficiency and competitive reconstruction accuracy. In the synthesis case, we also provide theoretical guarantees for the signal recovery that are on par with the existing perfect reconstruction bounds for the relaxation-based solvers and other sophisticated greedy algorithms.
Cite
@article{arxiv.2004.06661,
title = {Efficient Least Residual Greedy Algorithms for Sparse Recovery},
author = {Guy Leibovitz and Raja Giryes},
journal= {arXiv preprint arXiv:2004.06661},
year = {2020}
}
Comments
accepted to IEEE Transactions on Signal Processing