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Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) hasbecome the most popular algorithm for aspect modeling. While sufficientlysuccessful in text topic extraction from large corpora, VB is less successfulin identifying aspects in the presence of limited data. We present a novelvariational message passing algorithm as applied to Latent Dirichlet Allocation(LDA) and compare it with the gold standard VB and collapsed Gibbs sampling. Insituations where marginalisation leads to non-conjugate messages, we use ideasfrom sampling to derive approximate update equations. In cases where conjugacyholds, Loopy Belief update (LBU) (also known as Lauritzen-Spiegelhalter) isused. Our algorithm, ALBU (approximate LBU), has strong similarities withVariational Message Passing (VMP) (which is the message passing variant of VB).To compare the performance of the algorithms in the presence of limited data,we use data sets consisting of tweets and news groups. Additionally, to performmore fine grained evaluations and comparisons, we use simulations that enablecomparisons with the ground truth via Kullback-Leibler divergence (KLD). Usingcoherence measures for the text corpora and KLD with the simulations we showthat ALBU learns latent distributions more accurately than does VB, especiallyfor smaller data sets.

ALBU: An approximate Loopy Belief message passing algorithm for LDA to improve performance on small data sets