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

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|, x^2 or x^4 and shrinkage constants 0 or 1, many multiblock component methods are recovered.
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https://hal.archives-ouvertes.fr/hal-01630730
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Submitted on : Wednesday, November 8, 2017 - 11:41:50 AM
Last modification on : Wednesday, April 3, 2019 - 1:19:29 AM

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Michel Tenenhaus, Arthur Tenenhaus, Patrick J. F. Groenen. Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods. Psychometrika, Springer Verlag, 2017, 82 (3), pp.737-777. ⟨10.1007/s11336-017-9573-x⟩. ⟨hal-01630730⟩

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