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.

中文翻译：

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无需超参数交叉验证的准确贝叶斯数据分类

我们通过考虑共轭正态 Wishart 先验分布的泛化，并通过证据最大化分析推导超参数，扩展了标准贝叶斯多元高斯生成数据分类器。在高维数据机制中探索了最优超参数的行为。由此产生的广义模型的分类准确性与最先进的贝叶斯判别分析方法相比具有竞争力，但没有通常的交叉验证计算负担。