Tree-Structured Gaussian Mixture Models and Their Variational Inference

Yuta Nakahara

Abstract: In this paper, Gaussian mixture models with tree structure and their variational inference methods are proposed for non-parametric Bayesian clustering. In this model, the tree structure is included as an unobservable random variable. The number of leaf nodes corresponds to the number of mixture components. This model is expected to capture not only the number of clusters but also tree structure from data.