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.

在贝叶斯非参数设置中推断潜在特征模型通常很困难，尤其是在高维设置中，因为它通常需要从某些先验分布中提出特征。在集成易于处理的特殊情况下，我们可以根据预测可能性对新的特征分配进行采样。我们提出了一种新方法，通过基于数据提出特征位置来加速潜在变量模型推理的混合，而不是先验。首先，我们介绍了一种加速特征提议机制，我们展示了该机制是一种用于后验推理的有效 MCMC 算法。接下来，我们提出了一种近似推理策略来并行执行加速推理。