A dirichlet process mixture model for spherical data

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.