AUTHORS Michael Hughes Dae Il Kim Erik Sudderth
ABSTRACT
We introduce a new variational inference objective for hierarchical Dirichlet process admixture models. Our approach provides
novel and scalable algorithms for learning
nonparametric topic models of text documents and Gaussian admixture models of image patches. Improving on the point estimates of topic probabilities used in previous
work, we define full variational posteriors for
all latent variables and optimize parameters
via a novel surrogate likelihood bound. We
show that this approach has crucial advantages for data-driven learning of the num-
ber of topics. Via merge and delete moves
that remove redundant or irrelevant topics,
we learn compact and interpretable models
with less computation. Scaling to millions
of documents is possible using stochastic or
memoized variational updates.
CODE
Available online: bitbucket.org/michaelchughes/bnpy-dev/ |
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 Updating... Ċ Mike Hughes, Feb 16, 2015, 6:46 AM Ċ Mike Hughes, Feb 16, 2015, 6:46 AM
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