Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach

Abstract

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.

References in corpus (5)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Bayesian Agglomerative Clustering with Coalescents
• Hierarchical Clustering with Structural Constraints
• Hierarchical Clustering via Spreading Metrics
• Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility