Visualization of Extremely Sparse Contingency Table by Taxicab
  Correspondence Analysis: A Case Study of Textual Data
V. Choulakian J. Allard
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).