Variational bayesian dirichlet-multinomial allocation for exponential family mixtures

Authors:
Shipeng Yu;Kai Yu;Volker Tresp;Hans-Peter Kriegel
Affiliations:
Institute for Computer Science, University of Munich, Germany;Siemens Corporate Technology, Munich, Germany;Siemens Corporate Technology, Munich, Germany;Institute for Computer Science, University of Munich, Germany
Venue:
ECML'06 Proceedings of the 17th European conference on Machine Learning
Year:
2006

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Abstract

This paper studies a Bayesian framework for density modeling with mixture of exponential family distributions. Variational Bayesian Dirichlet-Multinomial allocation (VBDMA) is introduced, which performs inference and learning efficiently using variational Bayesian methods and performs automatic model selection. The model is closely related to Dirichlet process mixture models and demonstrates similar automatic model selection in the variational Bayesian context.