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

arXiv: https://arxiv.org/abs/2308.03079 · HTML: https://mlsanity.com/paper/2308.03079/

## 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.

## Builds on (most cited first, 6 of 6)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization](https://mlsanity.com/paper/2012.04456/) (2020)
- [Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunities](https://mlsanity.com/paper/2308.01475/) (2023)
- [A Spectral Method for Assessing and Combining Multiple Data Visualizations](https://mlsanity.com/paper/2210.13711/) (2022)
- [What do Asian Religions Have in Common? An Unsupervised Text Analytics Exploration](https://mlsanity.com/paper/1912.10847/) (2019)
- [Quantification of intrinsic quality of a principal dimension in correspondence analysis and taxicab correspondence analysis](https://mlsanity.com/paper/2108.10685/) (2021)

## 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).