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Journal Articles Psychometrika Year : 2011

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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Dates and versions

hal-00604496 , version 1 (29-06-2011)

Identifiers

  • HAL Id : hal-00604496 , version 1

Cite

Arthur Tenenhaus, Michel Tenenhaus. Regularized generalized canonical correlation analysis. Psychometrika, 2011, 76 (2), pp.257-284. ⟨hal-00604496⟩
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