Articles

Regularized generalized canonical correlation analysis

M. TENENHAUS, A. TENENHAUS

Psychometrika

avril 2011, vol. 76, n°2, pp.257-284

Départements : Economie et Sciences de la décision

Mots clés : Generalized canonical correlation analysis, Multi-block data analysis, PLS path modeling, Regularized canonical correlation analysis


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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