TY - GEN
T1 - Minimum complexity pursuit
T2 - 2012 IEEE International Symposium on Information Theory, ISIT 2012
AU - Jalali, Shirin
AU - Maleki, Arian
AU - Baraniuk, Richard
PY - 2012
Y1 - 2012
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.
UR - http://www.scopus.com/inward/record.url?scp=84867548409&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867548409&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2012.6283602
DO - 10.1109/ISIT.2012.6283602
M3 - Conference contribution
AN - SCOPUS:84867548409
SN - 9781467325790
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1857
EP - 1861
BT - 2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012
Y2 - 1 July 2012 through 6 July 2012
ER -