The Geometry of Signal Recovery
Part of a series of seminars at the Dept. of Mathematical Sciences, UNLV.
Speaker: Justin Le.
Host: Prof. David Costa.
Friday, Nov. 17, 2017
11:30am to 1:30pm
CBC C-222, UNLV
In this talk, we discuss a convex program for recovering a structured signal from random linear measurements. Our emphasis will be on the derivation of bounds on estimation error that are afforded by Gaussian randomness of the measurements. In particular, the use of Gordon's "escape" lemma allows us to compute bounds on the minimum conic singular value associated with the measurement operator and with the descent cone of a complexity measure that encourages a certain structure on the recovered signal. We discuss the role of Gaussian width (and statistical dimension), as well as recent efforts toward non-Gaussian and non-linear cases of the problem.
No slides are available. The talk is given on whiteboard to facilitate interaction with the audience when discussing the proofs.