An alternative prior process for nonparametric Bayesian clustering
Published
Journal Article
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 process: partitions are no longer ex-changeable 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. We compare performance on a real document clustering task, demonstrating the practical advantage of the uniform process despite its lack of exchangeability over orderings. Copyright 2010 by the authors.
Duke Authors
Cited Authors
- Wallach, HM; Jensen, ST; Dicker, L; Heller, KA
Published Date
- December 1, 2010
Published In
Volume / Issue
- 9 /
Start / End Page
- 892 - 899
Electronic International Standard Serial Number (EISSN)
- 1533-7928
International Standard Serial Number (ISSN)
- 1532-4435
Citation Source
- Scopus