APPLIED MATH SEMINAR

Name:   Rachel Ward, Princeton University

Title:     Cross Validation in Compressed Sensing

When/where: Tuesday, November 25, 4:15PM, AKW 200

Abstract:          

The emerging area of Compressed Sensing revolves around the surprising fact that underdetermined linear systems of the form Ax = y can be efficiently solved if x is known a priori to be sufficiently sparse, that is, to have sufficiently small support.  Most real world signals x are not exactly sparse, but rather approximately sparse, in which case only an approximation to the underlying signal x satisfying Ax = y can be found in general.  In this talk, we will show how cross validation-type techniques  used in statistics and learning theory naturally  apply in the Compressed Sensing setup and offer very efficient tight bounds on the root mean squared error ||x - x*|| between the approximation x* and underlying signal x.  Cross Validation techniques can be used for parameter selection in Compressed Sensing as well.