Robust 1-bit Compressive Sensing via Gradient Support Pursuit
Information Theory
2013-04-25 v1 math.IT
Optimization and Control
Statistics Theory
Statistics Theory
Abstract
This paper studies a formulation of 1-bit Compressed Sensing (CS) problem based on the maximum likelihood estimation framework. In order to solve the problem we apply the recently proposed Gradient Support Pursuit algorithm, with a minor modification. Assuming the proposed objective function has a Stable Restricted Hessian, the algorithm is shown to accurately solve the 1-bit CS problem. Furthermore, the algorithm is compared to the state-of-the-art 1-bit CS algorithms through numerical simulations. The results suggest that the proposed method is robust to noise and at mid to low input SNR regime it achieves the best reconstruction SNR vs. execution time trade-off.
Cite
@article{arxiv.1304.6627,
title = {Robust 1-bit Compressive Sensing via Gradient Support Pursuit},
author = {Sohail Bahmani and Petros T. Boufounos and Bhiksha Raj},
journal= {arXiv preprint arXiv:1304.6627},
year = {2013}
}
Comments
18 pages, 5 figures