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

M Tenenhaus, A Tenenhaus, Patrick Groenen

Research output: Contribution to journalArticleAcademicpeer-review

63 Citations (Scopus)
57 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)737-777
Number of pages41
JournalPsychometrika
Volume82
Issue number3
DOIs
Publication statusPublished - 30 May 2017

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