Published July 2012
| public
Book Section - Chapter
Minimum Complexity Pursuit: Stability Analysis
- Creators
- Jalali, Shirin
- Maleki, Arian
- Baraniuk, Richard
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.
Additional Information
© 2012 IEEE. Date of Current Version: 27 August 2012.Additional details
- Eprint ID
- 33622
- DOI
- 10.1109/ISIT.2012.6283602
- Resolver ID
- CaltechAUTHORS:20120828-150935773
- Created
-
2012-08-29Created from EPrint's datestamp field
- Updated
-
2021-11-09Created from EPrint's last_modified field
- Series Name
- IEEE International Symposium on Information Theory Proceedings