On multi-set canonical correlation analysis

Mohammed A. Hasan

doi:10.1109/IJCNN.2009.5178958

On multi-set canonical correlation analysis

Mohammed A. Hasan

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

25 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 language	English (US)
Title of host publication	2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages	1128-1133
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2009.5178958
State	Published - 2009
Event	2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States Duration: Jun 14 2009 → Jun 19 2009

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Other

Other	2009 International Joint Conference on Neural Networks, IJCNN 2009
Country/Territory	United States
City	Atlanta, GA
Period	6/14/09 → 6/19/09

Keywords

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

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Hasan, MA 2009, On multi-set canonical correlation analysis. in 2009 International Joint Conference on Neural Networks, IJCNN 2009., 5178958, Proceedings of the International Joint Conference on Neural Networks, pp. 1128-1133, 2009 International Joint Conference on Neural Networks, IJCNN 2009, Atlanta, GA, United States, 6/14/09. https://doi.org/10.1109/IJCNN.2009.5178958

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AB - 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.

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KW - Generalized eigenvalue problem

KW - Lyapunov stability

KW - Multi-set canonical correlation analysis

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ER -

On multi-set canonical correlation analysis

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