Robust Geodesic Regression

@article{Shin2022RobustGR,
  title={Robust Geodesic Regression},
  author={Ha-Young Shin and Hee‐Seok Oh},
  journal={Int. J. Comput. Vis.},
  year={2022},
  volume={130},
  pages={478-503}
}
This paper studies robust regression for data on Riemannian manifolds. Geodesic regression is the generalization of linear regression to a setting with a manifold-valued dependent variable and one or more real-valued independent variables. The existing work on geodesic regression uses the sum-of-squared errors to find the solution, but as in the classical Euclidean case, the least-squares method is highly sensitive to outliers. In this paper, we use M-type estimators, including the $L_1$, Huber… 
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