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• W4387339743 startingPage "102045" @default.
• W4387339743 abstract "Regularized Generalized Canonical Correlation Analysis (RGCCA) is a general statistical framework for multiblock data analysis. RGCCA enables deciphering relationships between several sets of variables and subsumes many well-known multivariate analysis methods as special cases. However, RGCCA only deals with vector-valued blocks, disregarding their possible higher-order structures. This paper presents Tensor GCCA (TGCCA), a new method for analyzing higher-order tensors with canonical vectors admitting an orthogonal rank-R CP decomposition. Moreover, two algorithms for TGCCA, based on whether a separable covariance structure is imposed or not, are presented along with convergence guarantees. The efficiency and usefulness of TGCCA are evaluated on simulated and real data and compared favorably to state-of-the-art approaches." @default.
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• W4387339743 date "2024-02-01" @default.
• W4387339743 modified "2023-10-18" @default.
• W4387339743 title "Tensor generalized canonical correlation analysis" @default.
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• W4387339743 doi "https://doi.org/10.1016/j.inffus.2023.102045" @default.
• W4387339743 hasPublicationYear "2024" @default.
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