On multi-set canonical correlation analysis

Authors:
Mohammed A. Hasan
Affiliations:
Department of Electrical & Computer Engineering, University of Minnesota, Duluth, MN
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

Two-and multi-set canonical correlation analysis (CCA) and (MCCA) techniques are used to find linear combinations that give maximal multivariate differences. This paper describes methods for deriving MCCA dynamical systems which converge to the desired canonical variates and canonical correlations. Unconstrained and constrained optimization methods over quadratic constraints are applied to derive several dynamical systems that converge to a solution of a generalized eigenvalue problem. These include merit functions that are based on generalized Rayleigh quotient, and logarithmic generalized Rayleigh quotient.