Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation
Machine Learning
2015-11-19 v1 Information Theory
math.IT
Abstract
This letter proposes a low-computational Bayesian algorithm for noisy sparse recovery in the context of one bit compressed sensing with sensing matrix perturbation. The proposed algorithm which is called BHT-MLE comprises a sparse support detector and an amplitude estimator. The support detector utilizes Bayesian hypothesis test, while the amplitude estimator uses an ML estimator which is obtained by solving a convex optimization problem. Simulation results show that BHT-MLE algorithm offers more reconstruction accuracy than that of an ML estimator (MLE) at a low computational cost.
Cite
@article{arxiv.1511.05660,
title = {Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation},
author = {H. Zayyani and M. Korki and F. Marvasti},
journal= {arXiv preprint arXiv:1511.05660},
year = {2015}
}
Comments
2 pages, 1 figure