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Regularized Generalized Canonical Correlation Analysis

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.
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Contributor : Antoine Haldemann <>
Submitted on : Monday, July 18, 2011 - 3:39:38 PM
Last modification on : Wednesday, September 16, 2020 - 3:30:33 PM

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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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