Regularized Generalized Canonical Correlation Analysis - HEC Paris - École des hautes études commerciales de Paris Accéder directement au contenu
Article Dans Une Revue Psychometrika / Psychometrica Année : 2011

Regularized Generalized Canonical Correlation Analysis

Michel Tenenhaus
  • Fonction : Auteur
  • PersonId : 837037
Arthur Tenenhaus

Résumé

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.

Dates et versions

hal-00609220 , version 1 (18-07-2011)

Identifiants

Citer

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⟩

Collections

SUPELEC HEC
115 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More