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Advances in Data Analysis and Classification

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02-01-2023 | Regular Article

Sparse correspondence analysis for large contingency tables

Authors: Ruiping Liu, Ndeye Niang, Gilbert Saporta, Huiwen Wang

Published in: Advances in Data Analysis and Classification | Issue 4/2023

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Abstract

We propose sparse variants of correspondence analysis (CA) for large contingency tables like documents-terms matrices used in text mining. By seeking to obtain many zero coefficients, sparse CA remedies to the difficulty of interpreting CA results when the size of the table is large. Since CA is a double weighted PCA (for rows and columns) or a weighted generalized SVD, we adapt known sparse versions of these methods with specific developments to obtain orthogonal solutions and to tune the sparseness parameters. We distinguish two cases depending on whether sparseness is asked for both rows and columns, or only for one set.

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Title: Sparse correspondence analysis for large contingency tables
Authors: Ruiping Liu
Ndeye Niang
Gilbert Saporta
Huiwen Wang
Publication date: 02-01-2023
Publisher: Springer Berlin Heidelberg
Published in: Advances in Data Analysis and Classification / Issue 4/2023
Print ISSN: 1862-5347
Electronic ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-022-00531-5

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