Canonical correlations and generalized SVD: Applications and new algorithms

摘要

In this paper we consider canonical correlations and a generalization of the singular value decomposition (SVD) that involves three matrices. We show how the two matrix problems are related and how they can be used in important applications such as weighted least squares and optimal prediction. We present two new computational procedures for the problems based on implicit SVD methods for triple matrixproducts. Our algorithms are well suited for parallel implementation.