  Inference of latent feature models in the Bayesian nonparametric setting is
generally difficult, especially in high dimensional settings, because it
usually requires proposing features from some prior distribution. In special
cases, where the integration is tractable, we can sample new feature
assignments according to a predictive likelihood. We present a novel method to
accelerate the mixing of latent variable model inference by proposing feature
locations based on the data, as opposed to the prior. First, we introduce an
accelerated feature proposal mechanism that we show is a valid MCMC algorithm
for posterior inference. Next, we propose an approximate inference strategy to
perform accelerated inference in parallel. A two-stage algorithm that combines
the two approaches provides a computationally attractive method that can
quickly reach local convergence to the posterior distribution of our model,
while allowing us to exploit parallelization.
