An Alternative Prior Process for Nonparametric Bayesian Clustering


Hanna Wallach, Shane Jensen, Lee Dicker, Katherine Heller ;
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:892-899, 2010.


Prior distributions play a crucial role in Bayesian approaches to clustering. Two commonly-used prior distributions are the Dirichlet and Pitman-Yor processes. In this paper, we investigate the predictive probabilities that underlie these processes, and the implicit “rich-get-richer” characteristic of the resulting partitions. We explore an alternative prior for nonparametric Bayesian clustering, the uniform process, for applications where the “rich-get-richer” property is undesirable. We also explore the cost of this new process: partitions are no longer exchangeable with respect to the ordering of variables. We present new asymptotic and simulation-based results for the clustering characteristics of the uniform process and compare these with known results for the Dirichlet and Pitman-Yor processes. Finally, we compare performance on a real document clustering task, demonstrating the practical advantage of the uniform process despite its lack of exchangeability over orderings.

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