Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(1):288-296, 2014.
We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dirichlet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-speciﬁc contexts results in the nDP mixture over content variables. We provide a Polya-urn view of the model and an efﬁcient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.