Recovery of binary sparse signals from compressed linear measurements via polynomial optimization
Optimization and Control
2019-07-24 v1 Machine Learning
Signal Processing
Abstract
The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics. In the compressed sensing framework, tailored methods have been recently proposed to deal with the case of finite-valued sparse signals. In this work, we focus on binary sparse signals and we propose a novel formulation, based on polynomial optimization. This approach is analyzed and compared to the state-of-the-art binary compressed sensing methods.
Cite
@article{arxiv.1905.13181,
title = {Recovery of binary sparse signals from compressed linear measurements via polynomial optimization},
author = {Sophie M. Fosson and Mohammad Abuabiah},
journal= {arXiv preprint arXiv:1905.13181},
year = {2019}
}