  In latent Dirichlet allocation (LDA), topics are multinomial distributions
over the entire vocabulary. However, the vocabulary usually contains many words
that are not relevant in forming the topics. We adopt a variable selection
method widely used in statistical modeling as a dimension reduction tool and
combine it with LDA. In this variable selection model for LDA (vsLDA), topics
are multinomial distributions over a subset of the vocabulary, and by excluding
words that are not informative for finding the latent topic structure of the
corpus, vsLDA finds topics that are more robust and discriminative. We compare
three models, vsLDA, LDA with symmetric priors, and LDA with asymmetric priors,
on heldout likelihood, MCMC chain consistency, and document classification. The
performance of vsLDA is better than symmetric LDA for likelihood and
classification, better than asymmetric LDA for consistency and classification,
and about the same in the other comparisons.
