Regularized Generalized Canonical Correlation Analysis

Michel Tenenhaus () and Arthur Tenenhaus ()
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Michel Tenenhaus: HEC Paris - Recherche - Hors Laboratoire - HEC Paris - Ecole des Hautes Etudes Commerciales
Arthur Tenenhaus: E3S - Supélec Sciences des Systèmes - Ecole Supérieure d'Electricité - SUPELEC (FRANCE)

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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 analysis; multi-block data analysis; PLS path modeling; regularized canonical correlation analysis (search for similar items in EconPapers)
Date: 2011-04
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Citations: View citations in EconPapers (25)

Published in Psychometrika / Psychometrica, 2011, 76 (2), pp.257-284. ⟨10.1007/s11336-011-9206-8⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00609220

DOI: 10.1007/s11336-011-9206-8

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