Low rank and block low rank matrix approximation
Time and place
1 PM on Tuesday, May 3rd, 2011; NAC 1/511E
Arthur Szlam (Courant Institute)
Abstract
I will start by discussing the problem of finding a low rank approximation to a (perhaps large) matrix. I will describe a fast and provably accurate randomized method. I will then turn to ongoing work on simultaneously clustering the columns of the matrix into (not so many) blocks and finding a low rank approximation to each block.
