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

Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock... 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, $${\vert }x{\vert }$$ | x | , $$x^{2}$$ x 2 or $$x^{4}$$ x 4 and shrinkage constants 0 or 1, many multiblock component methods are recovered. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Psychometrika Springer Journals

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

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Publisher
Springer US
Copyright
Copyright © 2017 by The Psychometric Society
Subject
Psychology; Psychometrics; Assessment, Testing and Evaluation; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law; Statistical Theory and Methods
ISSN
0033-3123
eISSN
1860-0980
D.O.I.
10.1007/s11336-017-9573-x
Publisher site
See Article on Publisher Site

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, $${\vert }x{\vert }$$ | x | , $$x^{2}$$ x 2 or $$x^{4}$$ x 4 and shrinkage constants 0 or 1, many multiblock component methods are recovered.

Journal

PsychometrikaSpringer Journals

Published: May 23, 2017

References

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