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Regularized Generalized Canonical Correlation Analysis

Published online by Cambridge University Press:  01 January 2025

Arthur Tenenhaus  and
Michel Tenenhaus
Arthur Tenenhaus*
Affiliation:
Supelec, Gif-sur-Yvette
Michel Tenenhaus
Affiliation:
HEC Paris, Jouy-en-Josas
*
Requests for reprints should be sent to Arthur Tenenhaus, Department of Signal Processing and Electronics Systems, Supelec, Gif-sur-Yvette, 3 rue Joliot-Curie, Plateau de Moulon, 91192 Gif-sur-Yvette cedex, France. E-mail: arthur.tenenhaus@supelec.fr
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Abstract

Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to three or more sets of variables. It constitutes a general framework for many multi-block data analysis methods. It combines the power of multi-block data analysis methods (maximization of well identified criteria) and the flexibility of PLS path modeling (the researcher decides which blocks are connected and which are not). Searching for a fixed point of the stationary equations related to RGCCA, a new monotonically convergent algorithm, very similar to the PLS algorithm proposed by Herman Wold, is obtained. Finally, a practical example is discussed.

Keywords

generalized canonical correlation analysismulti-block data analysisPLS path modelingregularized canonical correlation analysis
Type
Original Paper
Information
Psychometrika , Volume 76 , Issue 2 , April 2011 , pp. 257 - 284
Copyright
Copyright © 2011 The Psychometric Society

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