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DESCRIPTION:Speaker: Tina Woolf, Claremont Graduate University
Talk Title: Weighted L1-Minimization for Sparse Recovery under Arbitrary Prior Information
Series: CommNetS
Abstract: Weighted L1-minimization has been studied as a technique for the reconstruction of a sparse signal from compressively sampled measurements when prior information about the signal, in the form of a support estimate, is available. In this talk, we present recent results on the recovery conditions and the associated recovery guarantees of weighted L1-minimization when arbitrarily many distinct weights are permitted. For example, such a setup might be used when one has multiple estimates for the support of a signal, and these estimates have varying degrees of accuracy. Our analysis yields an extension to existing works that assume only a single constant weight is used.
Biography: Tina Woolf is pursuing the Ph.D. degree in Mathematics at the Institute of Mathematical Sciences, Claremont Graduate University. She received her B.S. in Mathematics from California Polytechnic State University, San Luis Obispo in 2009 and her M.S. in Mathematics from Claremont Graduate University in 2012. Her research interests include compressed sensing, sparse approximation, and stochastic optimization. Since 2009, she has also been a Systems Engineer at Northrop Grumman, primarily involved in modeling, simulation, and payload system performance analysis of infrared systems.
Host: Mahdi Soltanolkotabi
SEQUENCE:5
DTSTART:20160804T140000
LOCATION:EEB 248
DTSTAMP:20160804T140000
SUMMARY:Communications, Networks & Systems (CommNetS) Seminar
UID:EC9439B1-FF65-11D6-9973-003065F99D04
DTEND:20160804T150000
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