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.
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.