Accelerated Gibbs sampling for the Indian buffet process
Finale Doshi and Zoubin Ghahramani
In: ICML 2009, 14-18 JUN 2009, Montreal, Quebec.
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.