| Literature DB >> 28536930 |
Michel Tenenhaus1, Arthur Tenenhaus2,3, Patrick J F Groenen4.
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, [Formula: see text], [Formula: see text] or [Formula: see text] and shrinkage constants 0 or 1, many multiblock component methods are recovered.Entities:
Keywords: GCCA; MAXBET; MAXDIFF; MAXVAR; PLS path modeling; RGCCA; SSQCOR; SUMCOR; consensus PCA; hierarchical PCA; multiblock component methods
Year: 2017 PMID: 28536930 DOI: 10.1007/s11336-017-9573-x
Source DB: PubMed Journal: Psychometrika ISSN: 0033-3123 Impact factor: 2.500