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

2023/08/06 by Vartan Choulakian, Choulakian, V., Jun Allard +1
Agricultural and Biological Sciences · #62H25 #62H30 #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sensory Analysis and Statistical Methods

paper · pdf · doi:10.48550/arxiv.2308.03079

openalex publication_date 2023/08/06 · openalex created_date 2023/08/09 · openalex updated_date 2026/07/28

Abstract

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).

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