Minimum complexity pursuit: Stability analysis

Shirin Jalali; Arian Maleki; Richard Baraniuk

doi:10.1109/ISIT.2012.6283602

Minimum complexity pursuit: Stability analysis

Shirin Jalali, Arian Maleki, Richard Baraniuk

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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)
Title of host publication	2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012
Pages	1857-1861
Number of pages	5
DOIs	https://doi.org/10.1109/ISIT.2012.6283602
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
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

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10.1109/ISIT.2012.6283602

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author = "Shirin Jalali and Arian Maleki and Richard Baraniuk",

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AU - Baraniuk, Richard

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N2 - 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.

AB - 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.

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BT - 2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012

Y2 - 1 July 2012 through 6 July 2012

ER -

Minimum complexity pursuit: Stability analysis

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