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ISTC-CC Abstract
Dependent Nonparametric Trees for Dynamic Hierarchical Clustering
Proceedings of 2014 Neural Information Processing Systems (NIPS’14), December 2014.
Avinava Dubey, Qirong Ho*, Sinead Williamson^, Eric P. Xing
Machine Learning Department, Carnegie Mellon University
*Institute for Infocomm Research, A*STAR
^McCombs School of Business, University of Texas at Austin
Hierarchical clustering methods offer an intuitive and powerful way to model a wide variety of data sets. However, the assumption of a fixed hierarchy is often overly restrictive when working with data generated over a period of time: We expect both the structure of our hierarchy, and the parameters of the clusters, to evolve with time. In this paper, we present a distribution over collections of time-dependent, infinite-dimensional trees that can be used to model evolving hierarchies, and present an efficient and scalable algorithm for performing approximate inference in such a model. We demonstrate the efficacy of our model and inference algorithm on both synthetic data and real-world document corpora.
FULL PAPER: pdf
