Departmental Papers (CIS)

Date of this Version


Document Type

Conference Paper


Appearing in Proceedings of the 24 th International Conference on Machine Learning, Corvallis, OR, 2007. Copyright 2007 by the author(s)/owner(s).


We introduce a new inference algorithm for Dirichlet process mixture models. While Gibbs sampling and variational methods focus on local moves, the new algorithm makes more global moves. This is done by introducing a permutation of the data points as an auxiliary variable. The algorithm is a blocked sampler which alternates between sampling the clustering and sampling the permutation. The key to the efficiency of this approach is that it is possible to use dynamic programming to consider all exponentially many clusterings consistent with a given permutation. We also show that random projections can be used to effectively sample the permutation. The result is a stochastic hill-climbing algorithm that yields burn-in times significantly smaller than those of collapsed Gibbs sampling.



Date Posted: 16 July 2012