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Regularized Generalized Canonical Correlation Analysis

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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.

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Authors and Affiliations

  1. Department of Signal Processing and Electronics Systems, Supelec, Gif-sur-Yvette, 3 rue Joliot-Curie, Plateau de Moulon, 91192, Gif-sur-Yvette cedex, France

    Arthur Tenenhaus

  2. HEC Paris, Jouy-en-Josas, France

    Michel Tenenhaus

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  1. Arthur Tenenhaus
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  2. Michel Tenenhaus
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Correspondence to Arthur Tenenhaus.

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Tenenhaus, A., Tenenhaus, M. Regularized Generalized Canonical Correlation Analysis. Psychometrika 76, 257–284 (2011). https://doi.org/10.1007/s11336-011-9206-8

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  • DOI: https://doi.org/10.1007/s11336-011-9206-8

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