We study a recent inferential framework, named posterior regularisation, on the Bayesian hierarchical mixture clustering (BHMC) model. This framework facilitates a simple way to impose extra constraints on a Bayesian model to overcome some weakness of the original model. It narrows the search space of the parameters of the Bayesian model through a formalism that imposes certain constraints on the features of the found solutions. In this paper, in order to enhance the separation of clusters, we apply posterior regularisation to impose max-margin constraints on the nodes at every level of the hierarchy. This paper shows how the framework integrates with BHMC and achieves the expected improvements over the original Bayesian model.

翻译：我们研究了最近关于巴伊西亚等级混合群集(BHMC)模式的推论框架,称为“后级常规化” 。这个框架为对巴伊西亚模式施加额外限制以克服原始模式的某些弱点提供了方便,通过对所发现解决方案的特点施加某些限制的形式主义缩小了巴伊西亚模式参数的搜索空间。在本文中,为了加强集群的分离,我们应用后级常规化对各级的节点施加最大界限限制。本文展示了框架如何与巴伊西亚模式融合,并实现对原巴伊西亚模式的预期改进。