@inproceedings{3c7f09673a914ae89a4181e1ddea4605,
title = "Corrupted sensing with sub-Gaussian measurements",
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.",
keywords = "Compressed sensing, Corrupted sensing, Extended matrix deviation inequality, Gaussian width, Signal separation, Sub-Gaussian",
author = "Jinchi Chen and Yulong Liu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Symposium on Information Theory, ISIT 2017 ; Conference date: 25-06-2017 Through 30-06-2017",
year = "2017",
month = aug,
day = "9",
doi = "10.1109/ISIT.2017.8006581",
language = "English",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "516--520",
booktitle = "2017 IEEE International Symposium on Information Theory, ISIT 2017",
address = "United States",
}