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.
https://hal-supelec.archives-ouvertes.fr/hal-00604496
Contributor : Karine El Rassi <>
Submitted on : Wednesday, June 29, 2011 - 10:38:26 AM Last modification on : Monday, December 14, 2020 - 12:38:04 PM Long-term archiving on: : Friday, September 30, 2011 - 2:21:41 AM