2022/12/26 by V. Masarotto, Masarotto, V., G. Masarotto +1
Computer Science · Medicine · #Advanced Neuroimaging Techniques and Applications #FOS: Computer and information sciences #Medical Imaging Techniques and Applications #Methodology (stat.ME) #Topological and Geometric Data Analysis
paper · pdf · doi:10.48550/arxiv.2212.13287
openalex publication_date 2022/12/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of clustering functional data according to their covariance structure. We contribute a soft clustering methodology based on the Wasserstein-Procrustes distance, where the in-between cluster variability is penalised by a term proportional to the entropy of the partition matrix. In this way, each covariance operator can be partially classified into more than one group. Such soft classification allows for clusters to overlap, and arises naturally in situations where the separation between all or some of the clusters is not well-defined. We also discuss how to estimate the number of groups and to test for the presence of any cluster structure. The algorithm is illustrated using simulated and real data. An R implementation is available in the Supplementary materials.