Regularized Generalized Canonical Correlation Analysis

Michel Tenenhaus (1) , Arthur Tenenhaus (2)

1 HEC Paris - Recherche - Hors Laboratoire
2 E3S - Supélec Sciences des Systèmes

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.

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

Domains: Operations management and information technology

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⟩