Date of Original Version

5-2014

Type

Conference Proceeding

Journal Title

Journal of Machine Learning Research : Workshop and Conference Proceedings

Volume

32

First Page

154

Last Page

162

Rights Management

Copyright 2014 by the author(s).

Abstract or Description

This paper proposes a suite of models for clustering high-dimensional data on a unit sphere based on von Mises-Fisher (vMF) distribution and for discovering more intuitive clusters than existing approaches. The proposed models include a) A Bayesian formulation of vMF mixture that enables information sharing among clusters, b) a Hierarchical vMF mixture that provides multiscale shrinkage and tree structured view of the data and c) a Temporal vMF mixture that captures evolution of clusters in temporal data. For posterior inference, we develop fast variational methods as well as collapsed Gibbs sampling techniques for all three models. Our experiments on six datasets provide strong empirical support in favour of vMF based clustering models over other popular tools such as K-means, Multinomial Mixtures and Latent Dirichlet Allocation.

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Published In

Journal of Machine Learning Research : Workshop and Conference Proceedings, 32, 154-162.