A dirichlet process mixture model for spherical data

Julian Straub, Jason Chang, Oren Freifeld, John W. Fisher

Research output: Contribution to journalConference articlepeer-review

28 Scopus citations


Directional data, naturally represented as points on the unit sphere, appear in many applications. However, unlike the case of Euclidean data, exible mixture models on the sphere that can capture correlations, handle an unknown number of components and ex-tend readily to high-dimensional data have yet to be suggested. For this purpose we propose a Dirichlet process mixture model of Gaussian distributions in distinct tangent spaces (DP-TGMM) to the sphere. Importantly, the formulation of the proposed model allows the extension of recent advances in efficient inference for Bayesian nonparametric models to the spherical domain. Experiments on synthetic data as well as real-world 3D surface normal and 20-dimensional semantic word vector data confirm the expressiveness and applicability of the DP-TGMM.

Original languageEnglish
Pages (from-to)930-938
Number of pages9
JournalJournal of Machine Learning Research
StatePublished - 1 Jan 2015
Externally publishedYes
Event18th International Conference on Artificial Intelligence and Statistics, AISTATS 2015 - San Diego, United States
Duration: 9 May 201512 May 2015

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


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