Accurate Bayesian Data Classification Without Hyperparameter Cross-Validation

Mansoor Sheikh () and A. C. C. Coolen
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Mansoor Sheikh: King’s College London
A. C. C. Coolen: King’s College London

Journal of Classification, 2020, vol. 37, issue 2, No 2, 277-297

Abstract: 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.

Keywords: Hyperparameters; Evidence maximization; Bayesian classification; High-dimensional data (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00357-019-09316-6

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