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A method for selecting the relevant dimensions for high-dimensional classification in singular vector spaces

Author

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  • Dawit G. Tadesse

    (Cincinnati Children’s Hospital Medical Center)

  • Mark Carpenter

    (Auburn University)

Abstract

In this paper, we give a new feature selection algorithm for the binary class classification problem in sparse high-dimensional spaces. Singular value decomposition (SVD) is a popular dimension reduction method in higher-dimensional classification. The traditional SVD method begins by ranking the Singular Dimensions (SDs) from largest singular value to the smallest. However, when the number of signals is fewer than the number of noise, the first few ranked SDs are not necessarily the best for classification. We demonstrate, theoretically and empirically, that our method efficiently selects the SDs most appropriate for classification and significantly reduces the misclassification error. We also apply our method to real data text mining applications.

Suggested Citation

  • Dawit G. Tadesse & Mark Carpenter, 2019. "A method for selecting the relevant dimensions for high-dimensional classification in singular vector spaces," 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. 13(2), pages 405-426, June.
    • Handle: RePEc:spr:advdac:v:13:y:2019:i:2:d:10.1007_s11634-018-0311-8
    DOI: 10.1007/s11634-018-0311-8
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    References listed on IDEAS

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