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Rethinking Collapsed Variational Bayes Inference for LDA

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 نشر من قبل Issei Sato
 تاريخ النشر 2012
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 تأليف Issei Sato




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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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