Multiway generalized canonical correlation analysis - Archive ouverte HAL
Article Dans Une Revue Biostatistics Année : 2020

Multiway generalized canonical correlation analysis

Résumé

Regularized generalized canonical correlation analysis (RGCCA) is a general multiblock data analysis framework that encompasses several important multivariate analysis methods such as principal component analysis, partial least squares regression, and several versions of generalized canonical correlation analysis. In this article, we extend RGCCA to the case where at least one block has a tensor structure. This method is called multiway generalized canonical correlation analysis (MGCCA). Convergence properties of the MGCCA algorithm are studied, and computation of higher-level components are discussed. The usefulness of MGCCA is shown on simulation and on the analysis of a cognitive study in human infants using electroencephalography (EEG).

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Arnaud Gloaguen, Cathy Philippe, Vincent Frouin, Giulia Gennari, Ghislaine Dehaene-Lambertz, et al.. Multiway generalized canonical correlation analysis. Biostatistics, 2020, 23 (1), pp.240-256. ⟨10.1093/biostatistics/kxaa010⟩. ⟨hal-03111117⟩
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