Sparse correspondence analysis for large contingency tables

Ruiping Liu (), Ndeye Niang (), Gilbert Saporta () and Huiwen Wang ()
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Ruiping Liu: Beijing Information Science and Technology University
Ndeye Niang: Conservatoire national des arts et métiers
Gilbert Saporta: Conservatoire national des arts et métiers
Huiwen Wang: Beihang University

Advances in Data Analysis and Classification, 2023, vol. 17, issue 4, No 8, 1037-1056

Abstract: 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.

Keywords: Contingency tables; High-dimensional data; Correspondence analysis; Sparsity; Textual data; Penalized matrix decomposition; 62H17; 62H25 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11634-022-00531-5

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