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Sparse correspondence analysis for large contingency tables

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ruiping Liu & Ndeye Niang & Gilbert Saporta & Huiwen Wang, 2023. "Sparse correspondence analysis for large contingency tables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(4), pages 1037-1056, December.
    • Handle: RePEc:spr:advdac:v:17:y:2023:i:4:d:10.1007_s11634-022-00531-5
    DOI: 10.1007/s11634-022-00531-5
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