Compressed Sensing for Manifold Data

Tuesday, March 13, 2012 - 3:00pm - 4:00pm
Lind 305
Mark Iwen (Duke University)
We will discuss techniques for approximating a point in high-dimensional Euclidean space which is close to a known low-dimensional compact submanifold when only a compressed linear sketch of the point is available. More specifically, given a point, x, in R^D we will consider linear measurement operators, M: R^D -> R^m, which have associated nonlinear inverses, A: R^m -> R^D, so that x - A(Mx) is small even when m