Learning Mixtures of Gaussian Processes through Random Projection

Abstract

We propose an ensemble clustering framework to uncover latent cluster labels in functional data generated from a Gaussian process mixture. Our method exploits the fact that the projection coefficients of the functional data onto any give projection function follow a univariate Gaussian mixture model (GMM). By conducting multiple one-dimensional projections and learning a uni- variate GMM for each, we create an ensemble of GMMs. Each GMM serves as a base cluster- ing, and applying ensemble clustering yields a consensus clustering. Our approach significantly reduces computational complexity compared to state-of-the-art methods, and we provide theoretical guarantees on the identifiability and learnability of Gaussian process mixtures. Extensive experiments on synthetic and real datasets confirm the superiority of our method over existing techniques