Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation

TL;DR

This paper introduces a stochastic collapsed variational Bayesian inference algorithm for LDA that is simpler, faster, and often yields better solutions than previous methods, enabling efficient large-scale and interactive topic modeling.

Contribution

It presents a novel stochastic collapsed variational Bayesian inference algorithm for LDA, improving efficiency and convergence over existing methods.

Findings

Faster convergence on large-scale corpora

Often finds better solutions than previous methods

Enables real-time topic modeling in interactive applications

Abstract

In the internet era there has been an explosion in the amount of digital text information available, leading to difficulties of scale for traditional inference algorithms for topic models. Recent advances in stochastic variational inference algorithms for latent Dirichlet allocation (LDA) have made it feasible to learn topic models on large-scale corpora, but these methods do not currently take full advantage of the collapsed representation of the model. We propose a stochastic algorithm for collapsed variational Bayesian inference for LDA, which is simpler and more efficient than the state of the art method. We show connections between collapsed variational Bayesian inference and MAP estimation for LDA, and leverage these connections to prove convergence properties of the proposed algorithm. In experiments on large-scale text corpora, the algorithm was found to converge faster and often to a better solution than the previous method. Human-subject experiments also demonstrated that the method can learn coherent topics in seconds on small corpora, facilitating the use of topic models in interactive document analysis software.