A multivariate functional data clustering method using parsimonious cluster weighted models

Abstract

We propose a method for clustering multivariate functional linear regression data. Our approach extends multivariate cluster weighted models to functional data with multivariate functional response and predictors, based on the ideas used by the funHDDC method. To add model flexibility, we consider several two-component parsimonious models by combining the parsimonious models used for funHDDC with the Gaussian parsimonious clustering models family in. Parameter estimation is carried out within the expectation maximization (EM) algorithm framework. The proposed method outperforms funHDDC on simulated and real-world data.