Cluster-Specific Predictions with Multi-Task Gaussian Processes

A model involving Gaussian processes (GPs) is introduced to simultaneously handle multitask learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for functional data as well as a learning step for subsequent predictions for new tasks. The model is instantiated as a mixture of multi-task GPs with common mean processes. A variational EM algorithm is derived for dealing with the optimisation of the hyper-parameters along with the hyper-posteriors' estimation of latent variables and processes. We establish explicit formulas for integrating the mean processes and the latent clustering variables within a predictive distribution, accounting for uncertainty in both aspects. This distribution is defined as a mixture of cluster-specific GP predictions, which enhances the performance when dealing with group-structured data. The model handles irregular grids of observations and offers different hypotheses on the covariance structure for sharing additional information across tasks. The performances on both clustering and prediction tasks are assessed through various simulated scenarios and real data sets. The overall algorithm, called MAGMACLUST, is publicly available as an R package.

Automated summary

This paper introduces a model based on Gaussian processes (GPs) for simultaneously handling multitask learning, clustering, and prediction for multiple functional data. The model acts as a model-based clustering method for functional data and also serves as a learning step for subsequent predictions for new tasks. By considering uncertainty in both mean processes and latent clustering variables within a predictive distribution, the model improves performance when dealing with group-structured data.