Compressed Sensing Based on Random Symmetric Bernoulli Matrix
Information Theory
2012-12-18 v1 math.IT
Abstract
The task of compressed sensing is to recover a sparse vector from a small number of linear and non-adaptive measurements, and the problem of finding a suitable measurement matrix is very important in this field. While most recent works focused on random matrices with entries drawn independently from certain probability distributions, in this paper we show that a partial random symmetric Bernoulli matrix whose entries are not independent, can be used to recover signal from observations successfully with high probability. The experimental results also show that the proposed matrix is a suitable measurement matrix.
Cite
@article{arxiv.1212.3799,
title = {Compressed Sensing Based on Random Symmetric Bernoulli Matrix},
author = {Yi-Zheng Fan and Tao Huang and Ming Zhu},
journal= {arXiv preprint arXiv:1212.3799},
year = {2012}
}
Comments
arXiv admin note: text overlap with arXiv:0902.4394 by other authors