Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach
This addresses hierarchical clustering problems for data with complex latent structures, representing an incremental advancement in Bayesian nonparametric methods.
The paper tackles hierarchical non-overlapping clustering of datasets with complex latent mixture features by developing a novel Bayesian nonparametric method combining the nested Chinese Restaurant Process and Hierarchical Dirichlet Process, achieving solid empirical results on three datasets compared to existing algorithms.
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.