A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Yee Teh, David Newman, Max Welling


Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference pro- cedures like variational Bayes and Gibbs sampling have been found lacking. In this paper we propose the collapsed variational Bayesian inference algorithm for LDA, and show that it is computationally efficient, easy to implement and signifi- cantly more accurate than standard variational Bayesian inference for LDA.