funFMC: Overlapping Clustering for Functional Data

In applications such as neuroscience and environmental science, data are naturally modeled as multivariate functional data and often exhibit overlapping cluster structure.

Existing clustering methods for functional data typically impose mutually exclusive memberships and therefore fail to capture such structure.

We propose a latent factor model based approach with functional factors and a real-valued loading matrix that encodes potentially overlapping cluster memberships.

Under mild conditions, we establish identifiability of the loading matrix up to permutation, ensuring that the overlapping cluster structure is recoverable up to label switching.

We develop a procedure for estimating both the number of clusters and the associated cluster memberships.

This involves solving an infinite-dimensional regression problem in operators, whose solution is characterized using the inner product on the space of Hilbert-Schmidt operators and expressed in terms of real-valued matrices.

This formulation enables rigorous asymptotic analysis, and we establish a central limit theorem to facilitate statistical inference on overlapping cluster memberships.

We demonstrate the performance of our method using numerical studies and an application to functional magnetic resonance imaging data.