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Visualization of Extremely Sparse Contingency Table by Taxicab Correspondence Analysis: A Case Study of Textual Data

arXiv 2308.03079 ↗First posted 6 Aug 2023stat.ML cs.LGcited by 0

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Abstract (arXiv)

We present an overview of taxicab correspondence analysis, a robust variant of correspondence analysis, for visualization of extremely sparse ontingency tables. In particular we visualize an extremely sparse textual data set of size 590 by 8265 concerning fragments of 8 sacred books recently introduced by Sah and Fokou'e (2019) and studied quite in detail by (12 + 1) dimension reduction methods (t-SNE, UMAP, PHATE,...) by Ma, Sun and Zou (2022).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 6 Aug 2023.

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References · 6

  1. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  2. 2020Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualizationcited by 173
  3. 2023Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunitiescited by 7
  4. 2022A Spectral Method for Assessing and Combining Multiple Data Visualizationscited by 6
  5. 2019What do Asian Religions Have in Common? An Unsupervised Text Analytics Explorationcited by 3
  6. 2021Quantification of intrinsic quality of a principal dimension in correspondence analysis and taxicab correspondence analysiscited by 1

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