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Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings

Author

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  • Yugo Nakayama

    (University of Tsukuba)

  • Kazuyoshi Yata

    (University of Tsukuba)

  • Makoto Aoshima

    (University of Tsukuba)

Abstract

In this paper, we study asymptotic properties of nonlinear support vector machines (SVM) in high-dimension, low-sample-size settings. We propose a bias-corrected SVM (BC-SVM) which is robust against imbalanced data in a general framework. In particular, we investigate asymptotic properties of the BC-SVM having the Gaussian kernel and compare them with the ones having the linear kernel. We show that the performance of the BC-SVM is influenced by the scale parameter involved in the Gaussian kernel. We discuss a choice of the scale parameter yielding a high performance and examine the validity of the choice by numerical simulations and actual data analyses.

Suggested Citation

  • Yugo Nakayama & Kazuyoshi Yata & Makoto Aoshima, 2020. "Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(5), pages 1257-1286, October.
    • Handle: RePEc:spr:aistmt:v:72:y:2020:i:5:d:10.1007_s10463-019-00727-1
    DOI: 10.1007/s10463-019-00727-1
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    References listed on IDEAS

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    1. Peter Hall & J. S. Marron & Amnon Neeman, 2005. "Geometric representation of high dimension, low sample size data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 427-444, June.
    2. Makoto Aoshima & Kazuyoshi Yata, 2019. "Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 473-503, June.
    3. Marron, J.S. & Todd, Michael J. & Ahn, Jeongyoun, 2007. "Distance-Weighted Discrimination," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1267-1271, December.
    4. Jeongyoun Ahn & J. S. Marron, 2010. "The maximal data piling direction for discrimination," Biometrika, Biometrika Trust, vol. 97(1), pages 254-259.
    5. Makoto Aoshima & Kazuyoshi Yata, 2014. "A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(5), pages 983-1010, October.
    6. Peter Hall & Yvonne Pittelkow & Malay Ghosh, 2008. "Theoretical measures of relative performance of classifiers for high dimensional data with small sample sizes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 159-173, February.
    7. Qiao, Xingye & Zhang, Hao Helen & Liu, Yufeng & Todd, Michael J. & Marron, J. S., 2010. "Weighted Distance Weighted Discrimination and Its Asymptotic Properties," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 401-414.
    8. Jeongyoun Ahn & J. S. Marron & Keith M. Muller & Yueh-Yun Chi, 2007. "The high-dimension, low-sample-size geometric representation holds under mild conditions," Biometrika, Biometrika Trust, vol. 94(3), pages 760-766.
    9. Yao-Ban Chan & Peter Hall, 2009. "Scale adjustments for classifiers in high-dimensional, low sample size settings," Biometrika, Biometrika Trust, vol. 96(2), pages 469-478.
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    Cited by:

    1. Ishii, Aki & Yata, Kazuyoshi & Aoshima, Makoto, 2022. "Geometric classifiers for high-dimensional noisy data," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    2. Nakayama, Yugo & Yata, Kazuyoshi & Aoshima, Makoto, 2021. "Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings," Journal of Multivariate Analysis, Elsevier, vol. 185(C).

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