Robust Geodesic Regression
This work addresses outlier sensitivity in geodesic regression for applications like neuroimaging, but it is incremental as it adapts existing robust estimators to a manifold setting.
The paper tackles robust regression for data on Riemannian manifolds by using M-type estimators like L1, Huber, and Tukey biweight to reduce sensitivity to outliers compared to least-squares methods, demonstrating promising empirical results in numerical examples including neuroimaging data.
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 and Tukey biweight estimators, to perform robust geodesic regression, and describe how to calculate the tuning parameters for the latter two. We also show that, on compact symmetric spaces, all M-type estimators are maximum likelihood estimators, and argue for the overall superiority of the $L_1$ estimator over the $L_2$ and Huber estimators on high-dimensional manifolds and over the Tukey biweight estimator on compact high-dimensional manifolds. Results from numerical examples, including analysis of real neuroimaging data, demonstrate the promising empirical properties of the proposed approach.