Clustering Functional Data via Variational Inference

2022/05/27 by Chengqian Xian, Camila P. E. de Souza, Xian, Chengqian +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning in Healthcare #Metabolomics and Mass Spectrometry Studies #Methodology (stat.ME) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2205.13716

openalex publication_date 2022/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Functional data analysis deals with data recorded densely over time (or any other continuum) with one or more observed curves per subject. Conceptually, functional data are continuously defined, but in practice, they are usually observed at discrete points. Among different kinds of functional data analyses, clustering analysis aims to determine underlying groups of curves in the dataset when there is no information on the group membership of each individual curve. In this work, we propose a new model-based approach for clustering and smoothing functional data simultaneously via variational inference. We derive coordinate ascent mean-field variational Bayes algorithms to approximate the posterior distribution of our model parameters by finding the variational distribution with the smallest Kullback-Leibler divergence to the posterior. The performance of our proposed method is evaluated using simulated data and publicly available datasets.

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• Model-Based Clustering of Functional Data Via Random Projection Ensembles

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