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arXiv · 2608.06971

Mixture of Geodesic Factor Analyzers on Riemannian Homogeneous Spaces

Hengchao Chen·Yuanyao Tan·Chao Huang·Hongtu Zhu·Qiang Sun
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Abstract

This paper introduces Mixtures of Geodesic Factor Analyzers (MGFA) on Riemannian homogeneous spaces. MGFA uses a geodesic factor model within each mixture component, providing greater expressiveness than mixtures of Riemannian radial distributions and enabling clustering of manifold-valued data with anisotropic subpopulations. We establish root-$n$ consistency for the MGFA maximum likelihood estimator (MLE), thereby filling a theoretical gap for mixtures of Riemannian radial distributions as a special case. We also propose an iterative estimation algorithm and implement it on spheres, shape spaces, and hyperbolic spaces. Numerical experiments show that MGFA substantially outperforms competing methods in well-specified regimes while remaining robust under model misspecification. Finally, case studies on corpus callosum and left hippocampus shape datasets demonstrate MGFA's effectiveness for both 2D contour and 3D shape analysis.

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Hengchao Chen, Yuanyao Tan, Chao Huang, Hongtu Zhu, Qiang Sun. 2026-08-07. Mixture of Geodesic Factor Analyzers on Riemannian Homogeneous Spaces. https://arxiv.org/abs/2608.06971

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