Functional Generalized Canonical Correlation Analysis for studying multiple longitudinal variables

• URL: http://arxiv.org/abs/2310.07330v1
• Date: Wed, 11 Oct 2023 09:21:31 GMT
• Title: Functional Generalized Canonical Correlation Analysis for studying multiple longitudinal variables
• Authors: Lucas Sort, Laurent Le Brusquet, Arthur Tenenhaus
• Abstract summary: Functional Generalized Canonical Correlation Analysis (FGCCA) is a new framework for exploring associations between multiple random processes observed jointly. We establish the monotonic property of the solving procedure and introduce a Bayesian approach for estimating canonical components. We present a use case on a longitudinal dataset and evaluate the method's efficiency in simulation studies.
• Score: 0.9208007322096533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce Functional Generalized Canonical Correlation Analysis (FGCCA), a new framework for exploring associations between multiple random processes observed jointly. The framework is based on the multiblock Regularized Generalized Canonical Correlation Analysis (RGCCA) framework. It is robust to sparsely and irregularly observed data, making it applicable in many settings. We establish the monotonic property of the solving procedure and introduce a Bayesian approach for estimating canonical components. We propose an extension of the framework that allows the integration of a univariate or multivariate response into the analysis, paving the way for predictive applications. We evaluate the method's efficiency in simulation studies and present a use case on a longitudinal dataset.

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