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Regularized Multiple-Set Canonical Correlation Analysis

Published online by Cambridge University Press:  01 January 2025

Yoshio Takane ,
Heungsun Hwang  and
Hervé Abdi
Yoshio Takane*
Affiliation:
McGill University
Heungsun Hwang
Affiliation:
McGill University
Hervé Abdi
Affiliation:
University of Texas at Dallas
*
Requests for reprints should be sent to Yoshio Takane, Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1 Canada. E-mail: takane@psych.mcgill.ca
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Abstract

Multiple-set canonical correlation analysis (Generalized CANO or GCANO for short) is an important technique because it subsumes a number of interesting multivariate data analysis techniques as special cases. More recently, it has also been recognized as an important technique for integrating information from multiple sources. In this paper, we present a simple regularization technique for GCANO and demonstrate its usefulness. Regularization is deemed important as a way of supplementing insufficient data by prior knowledge, and/or of incorporating certain desirable properties in the estimates of parameters in the model. Implications of regularized GCANO for multiple correspondence analysis are also discussed. Examples are given to illustrate the use of the proposed technique.

Keywords

information integrationprior informationridge regressiongeneralized singular value decomposition (GSVD)G-fold cross validationpermutation teststhe Bootstrap methodmultiple correspondence analysis (MCA)

Information

Type
Original Paper
Information
Psychometrika , Volume 73 , Issue 4 , December 2008 , pp. 753 - 775
Copyright
Copyright © 2008 The Psychometric Society

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Footnotes

The work reported in this paper is supported by Grants 10630 and 290439 from the Natural Sciences and Engineering Research Council of Canada to the first and the second authors, respectively. The authors would like to thank the two editors (old and new), the associate editor, and four anonymous reviewers for their insightful comments on earlier versions of this paper. Matlab programs that carried out the computations reported in the paper are available upon request.

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