Posterior Regularisation on Bayesian Hierarchical Mixture Clustering

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.

Bayesian Hierarchical Mixture Clustering using Multilevel Hierarchical Dirichlet Processes

This paper focuses on the problem of hierarchical non-overlapping clustering of a dataset. In such a clustering, each data item is associated with exactly one leaf node and each internal node is associated with all the data items stored in the sub-tree beneath it, so that each level of the hierarchy corresponds to a partition of the dataset. We develop a novel Bayesian nonparametric method combining the nested Chinese Restaurant Process (nCRP) and the Hierarchical Dirichlet Process (HDP). Compared with other existing Bayesian approaches, our solution tackles data with complex latent mixture features which has not been previously explored in the literature. We discuss the details of the model and the inference procedure. Furthermore, experiments on three datasets show that our method achieves solid empirical results in comparison with existing algorithms.