Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts

TL;DR

This paper introduces a Bayesian nonparametric multilevel clustering model that leverages group-level context to improve the discovery of structures and group partitions, with efficient inference and demonstrated advantages in real-world data.

Contribution

It develops a novel nested Dirichlet process framework that integrates content and context at multiple levels, linking nDP and DPM models in a unified approach.

Findings

Improved clustering accuracy using context information.

Effective inference via collapsed Gibbs sampling.

Enhanced performance on text and image datasets.

Abstract

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dirichlet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-specific contexts results in the nDP mixture over content variables. We provide a Polya-urn view of the model and an efficient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.