A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior

TL;DR

This paper introduces a Bayesian supervised clustering model using the Dirichlet process prior, capable of handling infinite sets and outperforming existing methods on various tasks through MCMC inference.

Contribution

The paper presents a novel Bayesian supervised clustering framework with Dirichlet process prior and MCMC inference, applicable to multiple real-world tasks.

Findings

Outperforms existing algorithms on real-world datasets

Handles infinite clustering scenarios effectively

Demonstrates flexibility with conjugate and non-conjugate priors

Abstract

We develop a Bayesian framework for tackling the supervised clustering problem, the generic problem encountered in tasks such as reference matching, coreference resolution, identity uncertainty and record linkage. Our clustering model is based on the Dirichlet process prior, which enables us to define distributions over the countably infinite sets that naturally arise in this problem. We add supervision to our model by positing the existence of a set of unobserved random variables (we call these "reference types") that are generic across all clusters. Inference in our framework, which requires integrating over infinitely many parameters, is solved using Markov chain Monte Carlo techniques. We present algorithms for both conjugate and non-conjugate priors. We present a simple--but general--parameterization of our model based on a Gaussian assumption. We evaluate this model on one artificial task and three real-world tasks, comparing it against both unsupervised and state-of-the-art supervised algorithms. Our results show that our model is able to outperform other models across a variety of tasks and performance metrics.