The Higher Order Generalized Singular Value Decomposition

Author: 
Charles Van Loan
Date: 
Thu, Aug 8, 2013 - Sat, Aug 10, 2013
Location: 
PIMS, University of British Columbia
Conference: 
Workshop on Numerical Linear Algebra and Optimization
Abstract: 
Suppose you have a collection of data matrices each of which has the same number of columns. The HO-GSVD can be used to identify common features that are implicit across the collection. It works by identifying a certain (approximate) invariant subspace of a matrix that is a challenging combination of the collection matrices. In describing the computational process I will talk about the Higher Order CS decomposition and a really weird optimization problem that I bet you have never seen before! Joint work with Orly Alter, Priya Ponnapalli, and Mike Saunders.
Notes: