, 76:257 | Cite as

Regularized Generalized Canonical Correlation Analysis

  • Arthur Tenenhaus
  • Michel Tenenhaus


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.


generalized canonical correlation analysis multi-block data analysis PLS path modeling regularized canonical correlation analysis 


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Copyright information

© The Psychometric Society 2011

Authors and Affiliations

  1. 1.Department of Signal Processing and Electronics SystemsSupelec, Gif-sur-YvetteGif-sur-Yvette cedexFrance
  2. 2.HEC ParisJouy-en-JosasFrance

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