Articles

Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods

M. TENENHAUS, A. TENENHAUS, P. J. F. GROENEN

Psychometrika

septembre 2017, vol. 82, n°3, pp.737-777

Départements : Economie et Sciences de la décision

Mots clés : consensus PCA, hierarchical PCA, MAXBET, MAXDIFF, MAXVAR, multiblock component methods, PLS path modeling, GCCA, RGCCA, SSQCOR, SUMCOR

https://link.springer.com/article/10.1007/s11336-017-9573-x


A new framework for sequential multiblock component methods is presented. This framework relies on a new version of regularized generalized canonical correlation analysis (RGCCA) where various scheme functions and shrinkage constants are considered. Two types of between block connections are considered: blocks are either fully connected or connected to the superblock (concatenation of all blocks). The proposed iterative algorithm is monotone convergent and guarantees obtaining at convergence a stationary point of RGCCA. In some cases, the solution of RGCCA is the first eigenvalue / eigenvector of a certain matrix. For the scheme functions x, |x|, x2 or x4 and shrinkage constants 0 or 1, many multiblock component methods are recovered


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