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arXiv · 1206.6435

Rethinking Collapsed Variational Bayes Inference for LDA

Issei Sato·Hiroshi Nakagawa
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Abstract

We propose a novel interpretation of the collapsed variational Bayes inference with a zero-order Taylor expansion approximation, called CVB0 inference, for latent Dirichlet allocation (LDA). We clarify the properties of the CVB0 inference by using the alpha-divergence. We show that the CVB0 inference is composed of two different divergence projections: alpha=1 and -1. This interpretation will help shed light on CVB0 works.

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Issei Sato, Hiroshi Nakagawa. 2012-06-27. Rethinking Collapsed Variational Bayes Inference for LDA. https://arxiv.org/abs/1206.6435

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