Variational Inference for the Indian Buffet Process


Finale Doshi, Kurt Miller, Jurgen Van Gael, Yee Whye Teh ;
Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:137-144, 2009.


The Indian Buffet Process (IBP) is a nonparametric prior for latent feature models in which observations are influenced by a combination of several hidden features. For example, images may be composed of several objects or sounds may consist of several notes. Latent feature models seek to infer what these latent features from a set of observations. Current inference methods for the IBP have all relied on sampling. While these methods are guaranteed to be accurate in the limit, in practice, samplers for the IBP tend to mix slow. We develop a deterministic variational method for the IBP. We provide theoretical guarantees on its truncation bounds and demonstrate its superior performance for high dimensional data sets.

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