Clustering multivariate functional data using unsupervised binary trees

Abstract

We propose a model-based clustering algorithm for a general class of functional data for which the components could be curves or images. The random functional data realizations could be measured with error at discrete, and possibly random, points in the definition domain. The idea is to build a set of binary trees by recursive splitting of the observations. The number of groups are determined in a data-driven way. The new algorithm provides easily interpretable results and fast predictions for online data sets. Results on simulated datasets reveal good performance in various complex settings. The methodology is applied to the analysis of vehicle trajectories on a German roundabout.

References in corpus (4)

• Defining probability density for a distribution of random functions
• Low-Rank Covariance Function Estimation for Multidimensional Functional Data
• Learning the smoothness of noisy curves with application to online curve estimation
• Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs

Cited by in corpus (1)

• FDApy: a Python package for functional data