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Accurate Bayesian Data Classification Without Hyperparameter Cross-Validation

Author

Listed:
  • Mansoor Sheikh

    (King’s College London
    King’s College London)

  • A. C. C. Coolen

    (King’s College London
    Saddle Point Science)

Abstract

We extend the standard Bayesian multivariate Gaussian generative data classifier by considering a generalization of the conjugate, normal-Wishart prior distribution, and by deriving the hyperparameters analytically via evidence maximization. The behaviour of the optimal hyperparameters is explored in the high-dimensional data regime. The classification accuracy of the resulting generalized model is competitive with state-of-the art Bayesian discriminant analysis methods, but without the usual computational burden of cross-validation.

Suggested Citation

  • Mansoor Sheikh & A. C. C. Coolen, 2020. "Accurate Bayesian Data Classification Without Hyperparameter Cross-Validation," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 277-297, July.
    • Handle: RePEc:spr:jclass:v:37:y:2020:i:2:d:10.1007_s00357-019-09316-6
    DOI: 10.1007/s00357-019-09316-6
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    References listed on IDEAS

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