Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
This work addresses computational bottlenecks for researchers and practitioners using topic models, offering an incremental improvement in parallel inference efficiency.
The authors tackled the problem of slow MCMC sampling in topic models by proposing a parallel sparse partially collapsed Gibbs sampler, which achieved faster speeds on larger corpora with minimal statistical inefficiency increase compared to state-of-the-art methods.
Topic models, and more specifically the class of Latent Dirichlet Allocation (LDA), are widely used for probabilistic modeling of text. MCMC sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties. In particular, we propose and compare two different strategies for sampling the parameter block with latent topic indicators. The experiments show that the increase in statistical inefficiency from only partial collapsing is smaller than commonly assumed, and can be more than compensated by the speedup from parallelization and sparsity on larger corpora. We also prove that the partially collapsed samplers scale well with the size of the corpus. The proposed algorithm is fast, efficient, exact, and can be used in more modeling situations than the ordinary collapsed sampler.