Thresholding Greedy Pursuit for Sparse Recovery Problems
Signal Processing
2021-03-23 v1 Information Theory
math.IT
Probability
Abstract
We study here sparse recovery problems in the presence of additive noise. We analyze a thresholding version of the CoSaMP algorithm, named Thresholding Greedy Pursuit (TGP). We demonstrate that an appropriate choice of thresholding parameter, even without the knowledge of sparsity level of the signal and strength of the noise, can result in exact recovery with no false discoveries as the dimension of the data increases to infinity.
Cite
@article{arxiv.2103.11893,
title = {Thresholding Greedy Pursuit for Sparse Recovery Problems},
author = {Hai Le and Alexei Novikov},
journal= {arXiv preprint arXiv:2103.11893},
year = {2021}
}
Comments
First version