CUR Decompositions and Applications
When
12:30 p.m., March 19, 2019
Speaker
Abstract
This talk will focus on an interesting matrix decomposition (CUR) which decomposes a matrix by selecting representative columns and rows from it. We give several equivalent formulations of this decomposition, and discuss randomized column and row sampling procedures which guarantee a valid CUR decomposition of a matrix is attained with high probability. We also discuss some perturbation results for the decomposition and illustrate some connections with applications including motion segmentation and facial recognition, as well as other data applications requiring dimensionality reduction as a first step.