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A Multilayer Correlated Topic Model

arXiv 2101.02028 ↗First posted 2 Jan 2021cs.IR cs.LG stat.COcited by 0

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Abstract (arXiv)

We proposed a novel multilayer correlated topic model (MCTM) to analyze how the main ideas inherit and vary between a document and its different segments, which helps understand an article's structure. The variational expectation-maximization (EM) algorithm was derived to estimate the posterior and parameters in MCTM. We introduced two potential applications of MCTM, including the paragraph-level document analysis and market basket data analysis. The effectiveness of MCTM in understanding the document structure has been verified by the great predictive performance on held-out documents and intuitive visualization. We also showed that MCTM could successfully capture customers' popular shopping patterns in the market basket analysis.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 38 references that are in the map. First posted on arXiv 2 Jan 2021.

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References · 7

  1. 2016Variational Inference: A Review for Statisticianscited by 1,466
  2. 2013Black Box Variational Inferencecited by 317
  3. 2013Expectation Propagation for approximate Bayesian inferencecited by 172
  4. 2016Automatic Differentiation Variational Inferencecited by 159
  5. 2015Automatic Variational Inference in Stancited by 24
  6. 2016On clustering network-valued datacited by 9
  7. 2019Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matchingcited by 3

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