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Journal Articles Psychometrika / Psychometrica Year : 2011

Regularized Generalized Canonical Correlation Analysis

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Michel Tenenhaus
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Arthur Tenenhaus

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.

Dates and versions

hal-00609220 , version 1 (18-07-2011)

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Michel Tenenhaus, Arthur Tenenhaus. Regularized Generalized Canonical Correlation Analysis. Psychometrika / Psychometrica, 2011, 76 (2), pp.257-284. ⟨10.1007/s11336-011-9206-8⟩. ⟨hal-00609220⟩

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