CMU-CALD-05-104
Center for Automated Learning and Discovery
School of Computer Science, Carnegie Mellon University


CMU-CALD-05-104

Time-Sensitive Dirichlet Process Mixture Models

Xiaojin Zhu, Zoubin Ghahramani, John Lafferty

May 2005

CMU-CALD-05-104.pdf


Keywords: I.2.6 [Artificial Intelligence]:Learning; I.5.1 [Pattern Recognition]: Models-Statistical, I.5.2 [Pattern Recognition]: DesignMethodology Classifier design and evaluation; General Terms: Algorithms; Additional Key Words: Dirichlet process mixture models, MCMC, time


We introduce Time-Sensitive Dirichlet Process Mixture models for clustering. The models allow infinite mixture components just like standard Dirichlet process mixture models. However they also have the ability to model time correlations between instances.

14 pages

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