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.
Original language | English (US) |
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Title of host publication | 2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012 |
Pages | 1857-1861 |
Number of pages | 5 |
DOIs | |
State | Published - Oct 22 2012 |
Event | 2012 IEEE International Symposium on Information Theory, ISIT 2012 - Cambridge, MA, United States Duration: Jul 1 2012 → Jul 6 2012 |
Other
Other | 2012 IEEE International Symposium on Information Theory, ISIT 2012 |
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Country | United States |
City | Cambridge, MA |
Period | 7/1/12 → 7/6/12 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Information Systems
- Modeling and Simulation
- Applied Mathematics