Accelerated sampling for the Indian Buffet Process

We often seek to identify co-occurring hidden features in a set of observations. The Indian Buffet Process (IBP) provides a nonparametric 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.

• Type: Journal
• Year: 2009
• Where: ICML
• Authors: Finale Doshi-Velez, Zoubin Ghahramani
• Keywords: Collapsed Gibbs Sampler, Gibbs Sampler, ICML 2009, Machine Learning, Uncollapsed Gibbs Sampler