PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Accelerated Gibbs Sampling for the Indian Buffet Process
Finale Doshi and Zoubin Ghahramani
In: International Conference on Machine Learning, 14-18 June 2009, Montreal, Canada.

Abstract

We often seek to identify co-occurring hidden features in a set of observations. The Indian Buffet Process (IBP) provides a non-parametric prior on the features present in each observation, but current inference techniques for the IBP often scale poorly. The collapsed Gibbs sampler for the IBP has a running time cubic in the number of observations, and the uncollapsed Gibbs sampler, while linear, is often slow to mix. We present a new linear-time collapsed Gibbs sampler for conjugate likelihood models and demonstrate its efficacy on large real-world datasets.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6522
Deposited By:Finale Doshi
Deposited On:08 March 2010