Summary of Graph Topic Modeling For Documents with Spatial or Covariate Dependencies, by Yeo Jin Jung et al.
Graph Topic Modeling for Documents with Spatial or Covariate Dependencies
by Yeo Jin Jung, Claire Donnat
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an extension to probabilistic latent semantic indexing (pLSI) for topic modeling that incorporates document-level metadata, such as covariates or known similarities between documents. This approach uses a graph formalism to model documents as nodes and edges representing similarities. The authors develop a new estimator based on fast graph-regularized iterative singular value decomposition (SVD), which encourages similar documents to share similar topic mixture proportions. They also derive high-probability bounds for the estimation error and design a specialized cross-validation method to optimize regularization parameters. Experimental results demonstrate improved performance and faster inference compared to existing Bayesian methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper takes a big step forward in understanding how to better group related documents together based on their content and other features. Currently, this process is tricky because it involves lots of complex math and computing. The authors come up with a new way to do this that’s faster and more accurate than previous methods. They use special graphs to connect similar documents and show that their approach works really well on both fake data and real-world examples. |
Keywords
» Artificial intelligence » Inference » Probability » Regularization
