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Robust relevance vector machine for classification with variational inference

Author

Listed:
  • Sangheum Hwang

    (Korea Advanced Institute of Science and Technology)

  • Myong K. Jeong

    (Rutgers, The State University of New Jersey)

Abstract

The relevance vector machine (RVM) is a widely employed statistical method for classification, which provides probability outputs and a sparse solution. However, the RVM can be very sensitive to outliers far from the decision boundary which discriminates between two classes. In this paper, we propose the robust RVM based on a weighting scheme, which is insensitive to outliers and simultaneously maintains the advantages of the original RVM. Given a prior distribution of weights, weight values are determined in a probabilistic way and computed automatically during training. Our theoretical result indicates that the influences of outliers are bounded through the probabilistic weights. Also, a guideline for determining hyperparameters governing a prior is discussed. The experimental results from synthetic and real data sets show that the proposed method performs consistently better than the RVM if a training data set is contaminated by outliers.

Suggested Citation

  • Sangheum Hwang & Myong K. Jeong, 2018. "Robust relevance vector machine for classification with variational inference," Annals of Operations Research, Springer, vol. 263(1), pages 21-43, April.
    • Handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-015-1890-9
    DOI: 10.1007/s10479-015-1890-9
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    References listed on IDEAS

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    1. YichaoWu, & Liu, Yufeng, 2007. "Robust Truncated Hinge Loss Support Vector Machines," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 974-983, September.
    2. Ormerod, J. T. & Wand, M. P., 2010. "Explaining Variational Approximations," The American Statistician, American Statistical Association, vol. 64(2), pages 140-153.
    3. Kyungsik Lee & Norman Kim & Myong Jeong, 2014. "The sparse signomial classification and regression model," Annals of Operations Research, Springer, vol. 216(1), pages 257-286, May.
    4. Sangheum Hwang & Dohyun Kim & Myong K Jeong & Bong-Jin Yum, 2015. "Robust kernel-based regression with bounded influence for outliers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1385-1398, August.
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    Cited by:

    1. O. Y. Bakhteev & V. V. Strijov, 2020. "Comprehensive analysis of gradient-based hyperparameter optimization algorithms," Annals of Operations Research, Springer, vol. 289(1), pages 51-65, June.

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