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Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process
Chong Wang · David Blei

Tue Dec 08 03:30 PM -- 03:31 PM (PST) @ None

We present a nonparametric hierarchical Bayesian model of document collections that decouples sparsity and smoothness in the component distributions (i.e., the ``topics). In the sparse topic model (STM), each topic is represented by a bank of selector variables that determine which terms appear in the topic. Thus each topic is associated with a subset of the vocabulary, and topic smoothness is modeled on this subset. We develop an efficient Gibbs sampler for the STM that includes a general-purpose method for sampling from a Dirichlet mixture with a combinatorial number of components. We demonstrate the STM on four real-world datasets. Compared to traditional approaches, the empirical results show that STMs give better predictive performance with simpler inferred models.

Author Information

Chong Wang (ByteDance Inc.)
David Blei (Columbia University)

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