Accelerated Variational Dirichlet Process Mixtures

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Kenichi Kurihara, Max Welling, Nikos Vlassis


Dirichlet Process (DP) mixture models are promising candidates for clustering applications where the number of clusters is unknown a priori. Due to compu- tational considerations these models are unfortunately unsuitable for large scale data-mining applications. We propose a class of deterministic accelerated DP mixture models that can routinely handle millions of data-cases. The speedup is achieved by incorporating kd-trees into a variational Bayesian algorithm for DP mixtures in the stick-breaking representation, similar to that of Blei and Jordan (2005). Our algorithm differs in the use of kd-trees and in the way we handle truncation: we only assume that the variational distributions are fixed at their pri- ors after a certain level. Experiments show that speedups relative to the standard variational algorithm can be significant.