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

TL;DR

This paper introduces ALBU, an approximate message passing algorithm for LDA that outperforms Variational Bayes in small data scenarios, improving the accuracy of latent distribution learning.

Contribution

The paper presents ALBU, a novel approximate message passing algorithm for LDA that enhances performance on limited data compared to existing methods.

Findings

ALBU learns latent distributions more accurately than VB on small datasets.

ALBU outperforms VB in coherence measures for text corpora.

ALBU provides better ground truth approximation in simulations using KLD.

Abstract

Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extraction from large corpora, VB is less successful in identifying aspects in the presence of limited data. We present a novel variational message passing algorithm as applied to Latent Dirichlet Allocation (LDA) and compare it with the gold standard VB and collapsed Gibbs sampling. In situations where marginalisation leads to non-conjugate messages, we use ideas from sampling to derive approximate update equations. In cases where conjugacy holds, Loopy Belief update (LBU) (also known as Lauritzen-Spiegelhalter) is used. Our algorithm, ALBU (approximate LBU), has strong similarities with Variational 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 perform more fine grained evaluations and comparisons, we use simulations that enable comparisons with the ground truth via Kullback-Leibler divergence (KLD). Using coherence measures for the text corpora and KLD with the simulations we show that ALBU learns latent distributions more accurately than does VB, especially for smaller data sets.