Support vector machine and its bias correction in high-dimension, low-sample-size settings

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Abstract

In this paper, we consider asymptotic properties of the support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. We show that the hard-margin linear SVM holds a consistency property in which misclassification rates tend to zero as the dimension goes to infinity under certain severe conditions. We show that the SVM is very biased in HDLSS settings and its performance is affected by the bias directly. In order to overcome such difficulties, we propose a bias-corrected SVM (BC-SVM). We show that the BC-SVM gives preferable performances in HDLSS settings. We also discuss the SVMs in multiclass HDLSS settings. Finally, we check the performance of the classifiers in actual data analyses.

Journal

  • Journal of statistical planning and inference

    Journal of statistical planning and inference (191), 88-100, 2017-12

    Elsevier

Keywords

Codes

  • NII Article ID (NAID)
    120006406987
  • NII NACSIS-CAT ID (NCID)
    AA00253748
  • Text Lang
    ENG
  • Article Type
    journal article
  • ISSN
    0378-3758
  • Data Source
    IR 
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