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Dependent nonparametric trees for dynamic hierarchical clustering
Kumar Avinava Dubey · Qirong Ho · Sinead Williamson · Eric Xing

Mon Dec 08 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D #None

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.

Author Information

Kumar Avinava Dubey (Google Research)
Qirong Ho (Petuum, Inc.)
Sinead Williamson (University of Texas at Austin)
Eric Xing (Petuum Inc. / Carnegie Mellon University)

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