@inproceedings{781e63f00be44aabaaa6dfe89bb9a991,
title = "Minimum complexity pursuit: Stability analysis",
abstract = "A host of problems involve the recovery of structured signals from a dimensionality reduced representation such as a random projection; examples include sparse signals (compressive sensing) and low-rank matrices (matrix completion). Given the wide range of different recovery algorithms developed to date, it is natural to ask whether there exist {"}universal{"} algorithms for recovering {"}structured{"} signals from their linear projections. We recently answered this question in the affirmative in the noise-free setting. In this paper, we extend our results to the case of noisy measurements.",
author = "Shirin Jalali and Arian Maleki and Richard Baraniuk",
year = "2012",
doi = "10.1109/ISIT.2012.6283602",
language = "English (US)",
isbn = "9781467325790",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1857--1861",
booktitle = "2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012",
address = "United States",
note = "2012 IEEE International Symposium on Information Theory, ISIT 2012 ; Conference date: 01-07-2012 Through 06-07-2012",
}