Corrupted Sensing with Sub-Gaussian Measurements
Information Theory
2017-05-23 v1 math.IT
Abstract
This paper studies the problem of accurately recovering a structured signal from a small number of corrupted sub-Gaussian measurements. We consider three different procedures to reconstruct signal and corruption when different kinds of prior knowledge are available. In each case, we provide conditions for stable signal recovery from structured corruption with added unstructured noise. The key ingredient in our analysis is an extended matrix deviation inequality for isotropic sub-Gaussian matrices.
Cite
@article{arxiv.1705.07531,
title = {Corrupted Sensing with Sub-Gaussian Measurements},
author = {Jinchi Chen and Yulong Liu},
journal= {arXiv preprint arXiv:1705.07531},
year = {2017}
}
Comments
To appear in Proceedings of IEEE International Symposium on Information Theory 2017