Deep Dirichlet Process Mixture Models

Abstract

In this paper we propose the deep Dirichlet process mixture (DDPM) model, which is an unsupervised method that simultaneously performs clustering and feature learning. The traditional Dirichlet process mixture model can infer the number of mixture components, but its flexibility is restricted since the clustering is performed in the raw feature space. Our method alleviates this limitation by using the flow-based deep neural network to learn more expressive features. DDPM unifies Dirichlet processes and the flow-based model with Monte Carlo expectation-maximization, and uses Gibbs sampling to sample from the posterior. This combination allows our method to exploit the mutually beneficial relation between clustering and feature learning. The effectiveness of DDPM is demonstrated by thorough experiments in various synthetic and real-world datasets.