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

被引：64
|
作者：
Tenenhaus, Michel [1 ]
Tenenhaus, Arthur [2 ,3 ]
Groenen, Patrick J. F. [4 ]
机构：
[1] HEC Paris, Jouy En Josas, France
[2] Univ Paris Sud, L2S, Cent Supelec, Gif Sur Yvette, France
[3] Brain & Spine Inst, Paris, France
[4] Erasmus Univ, Rotterdam, Netherlands
来源：
PSYCHOMETRIKA | 2017年 / 82卷 / 03期
关键词：
consensus PCA; hierarchical PCA; MAXBET; MAXDIFF; MAXVAR; multiblock component methods; PLS path modeling; GCCA; RGCCA; SSQCOR; SUMCOR; PLS; VARIABLES; SETS; MODELS;
D O I：
10.1007/s11336-017-9573-x
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
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, , or and shrinkage constants 0 or 1, many multiblock component methods are recovered.
引用
收藏
页码：737 / 777
页数：41
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