On multi-set canonical correlation analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages1128-1133
Number of pages6
DOIs
StatePublished - Nov 18 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
Country/TerritoryUnited States
CityAtlanta, GA
Period6/14/096/19/09

Keywords

  • Constrained optimization
  • Generalized eigenvalue problem
  • Lyapunov stability
  • Multi-set canonical correlation analysis

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