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

J. Allard (Universit\'e de Moncton Canada); V. Choulakian

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

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arxiv 2308.03079 v1 pith:LMCUEGEA submitted 2023-08-06 stat.ML cs.LG

V. Choulakian , J. Allard (Université de Moncton Canada) This is my paper · ORCID

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keywords analysiscorrespondenceextremelysparsedatataxicabtextualvisualization

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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).

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