Scalable inference for logistic-normal topic models

Logistic-normal topic models can effectively discover correlation structures among latent topics. However, their inference remains a challenge because of the non-conjugacy between the logistic-normal prior and multinomial topic mixing proportions. Existing algorithms either make restricting mean-field assumptions or are not scalable to large-scale applications. This paper presents a partially col-lapsed Gibbs sampling algorithm that approaches the provably correct distribution by exploring the ideas of data augmentation. To improve time efficiency, we fur-ther present a parallel implementation that can deal with large-scale applications and learn the correlation structures of thousands of topics from millions of docu-ments. Extensive empirical results demonstrate the promise.