This paper presents a discriminative classifier for compositional data. This classifier is based on the posterior distribution of the Generalized Dirichlet which is the discriminative counterpart of Generalized Dirichlet mixture model. Moreover, following the mixture of experts paradigm, we proposed a hierarchical mixture of this classifier. In order to learn the models parameters, we use a variational approximation by deriving an upper-bound for the Generalized Dirichlet mixture. To the best of our knownledge, this is the first time this bound is proposed in the literature. Experimental results are presented for spam detection and color space identification.

翻译：本文提出了一种适用于成分数据的判别式分类器。该分类器基于广义狄利克雷后验分布，是广义狄利克雷混合模型的判别式对应物。此外，遵循专家混合范式的思路，我们提出了一种层次化混合结构将该分类器整合其中。为学习模型参数，我们采用变分近似方法，通过推导广义狄利克雷混合模型的上界实现参数估计。据我们所知，这是文献中首次提出该上界。实验结果表明，该模型在垃圾邮件检测和色彩空间识别任务中均取得了良好效果。