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CUR Decompositions with Random Sampling: Scientific Computing and Matrix Computations Seminar

Seminar: Scientific Computing | April 3 | 12:10-1 p.m. | 380 Soda Hall

Ming Gu, UC Berkeley

Electrical Engineering and Computer Sciences (EECS)

Given a (Semi-) Positive Definite matrix A, we consider the problem of computing its low-rank CUR approximations by uniformly sampling its columns randomly. We show that for matrices with low coherence (a concept to be defined at the talk), random sampling produces rank-revealing CUR approximations that are within a constant factor from optimal., 510-516-4321