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Scalable Inference for Logistic-Normal Topic Models
Jianfei Chen · Jun Zhu · Zi Wang · Xun Zheng · Bo Zhang

Thu Dec 05 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None

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 collapsed Gibbs sampling algorithm that approaches the provably correct distribution by exploring the ideas of data augmentation. To improve time efficiency, we further present a parallel implementation that can deal with large-scale applications and learn the correlation structures of thousands of topics from millions of documents. Extensive empirical results demonstrate the promise.

Author Information

Jianfei Chen (Tsinghua University)
Jun Zhu (Tsinghua University)
Zi Wang (Tsinghua University)
Xun Zheng (Carnegie Mellon University)
Bo Zhang (Tsinghua University)

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