Mixed Compressed Sensing Based on Random Graphs
Information Theory
2013-07-09 v1 math.IT
Abstract
Finding a suitable measurement matrix is an important topic in compressed sensing. Though the known random matrix, whose entries are drawn independently from a certain probability distribution, can be used as a measurement matrix and recover signal well, in most cases, we hope the measurement matrix imposed with some special structure. In this paper, based on random graph models, we show that the mixed symmetric random matrices, whose diagonal entries obey a distribution and non-diagonal entries obey another distribution, can be used to recover signal successfully with high probability.
Cite
@article{arxiv.1307.2117,
title = {Mixed Compressed Sensing Based on Random Graphs},
author = {Yi-Zheng Fan and Tao Huang and Ming Zhu},
journal= {arXiv preprint arXiv:1307.2117},
year = {2013}
}
Comments
10 pages, 3 figures. arXiv admin note: text overlap with arXiv:1212.3799