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ALBU: An Approximate Loopy Belief Message Passing Algorithm for LDA for Small Data Sets

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Intelligent Computing (SAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 506))

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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. Using coherence measures we show that ALBU learns latent distributions more accurately than does VB, especially for smaller data sets.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, 7602, RSA

    Rebecca M. C. Taylor & Johan A. du Preez

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  1. Rebecca M. C. Taylor
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  2. Johan A. du Preez
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Correspondence to Rebecca M. C. Taylor .

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  1. Saga University, Saga, Japan

    Kohei Arai

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Taylor, R.M.C., du Preez, J.A. (2022). ALBU: An Approximate Loopy Belief Message Passing Algorithm for LDA for Small Data Sets. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-031-10461-9_50

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