PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational Inference for the Indian Buffet Process
Finale Doshi, Kurt Tadayuki Miller, Jurgen van Gael and Yee Whye Teh
In: AISTATS 2009, 16-18 Apr 2009, Clearwater, Florida, USA.

Abstract

The Indian Buffet Process (IBP) is a nonparametric prior for latent feature models in which observations are influenced by a combination of hidden features. For example, images may be composed of several objects and sounds may consist of several notes. Latent feature models seek to infer these unobserved features from a set of observations; the IBP provides a principled prior in situations where the number of hidden features is unknown. Current inference methods for the IBP have all relied on sampling. While these methods are guaranteed to be accurate in the limit, samplers for the IBP tend to mix slowly in practice. We develop a deterministic variational method for inference in the IBP based on a truncated stick-breaking approximation, provide theoretical bounds on the truncation error, and evaluate our method in several data regimes.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6738
Deposited By:Yee Whye Teh
Deposited On:08 March 2010