Covariance-based soft clustering of functional data based on the Wasserstein-Procrustes metric

TL;DR

This paper introduces a novel soft clustering method for functional data based on the Wasserstein-Procrustes metric, allowing for overlapping clusters and handling ambiguous separations in covariance structures.

Contribution

It proposes a new covariance-based soft clustering approach using Wasserstein-Procrustes distance with entropy regularization, enabling partial cluster memberships.

Findings

Method effectively clusters simulated data with overlapping groups.

Application to real data demonstrates practical utility.

Algorithm estimates number of clusters and tests for structure.

Abstract

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.