Visualization of Extremely Sparse Contingency Table by Taxicab Correspondence Analysis: A Case Study of Textual Data
This work addresses visualization challenges for sparse textual data in fields like text analysis or religious studies, but it appears incremental as it applies an existing method to a new dataset.
The paper tackles the problem of visualizing extremely sparse contingency tables, specifically a 590 by 8265 textual dataset of sacred book fragments, using taxicab correspondence analysis, a robust variant of correspondence analysis.
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 Fokoué (2019) and studied quite in detail by (12 + 1) dimension reduction methods (t-SNE, UMAP, PHATE,...) by Ma, Sun and Zou (2022).