Regularized Multivariate Functional Principal Component Analysis

• URL: http://arxiv.org/abs/2306.13980v1
• Date: Sat, 24 Jun 2023 14:22:25 GMT
• Title: Regularized Multivariate Functional Principal Component Analysis
• Authors: Hossein Haghbin, Yue Zhao, and Mehdi Maadooliat
• Abstract summary: This paper introduces a novel approach called regularized theCA (ReCA) to address the issue of controlling the roughness of Principal Components. The proposed method generates multivariate functional PCs, providing a concise and interpretable representation of the data.
• Score: 3.4238565157486187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional data. However, controlling the roughness of the extracted Principal Components (PCs) can be challenging. This paper introduces a novel approach called regularized MFPCA (ReMFPCA) to address this issue and enhance the smoothness and interpretability of the multivariate functional PCs. ReMFPCA incorporates a roughness penalty within a penalized framework, using a parameter vector to regulate the smoothness of each functional variable. The proposed method generates smoothed multivariate functional PCs, providing a concise and interpretable representation of the data. Extensive simulations and real data examples demonstrate the effectiveness of ReMFPCA and its superiority over alternative methods. The proposed approach opens new avenues for analyzing and uncovering relationships in complex multivariate functional datasets.

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