Bi-invariant Geodesic Regression with Data from the Osteoarthritis Initiative

TL;DR

This paper introduces a novel bi-invariant geodesic regression method on Lie groups, respecting symmetries in data, with applications to osteoarthritis progression analysis in clinical datasets.

Contribution

It develops a non-metric estimator for geodesic regression on Lie groups using affine connections, ensuring invariance under group translations, and proposes an efficient fixed point algorithm for computation.

Findings

Successfully applied to osteoarthritis dataset

Demonstrated invariance under group symmetries

Provided an efficient computational algorithm

Abstract

Many phenomena are naturally characterized by measuring continuous transformations such as shape changes in medicine or articulated systems in robotics. Modeling the variability in such datasets requires performing statistics on Lie groups, that is, manifolds carrying an additional group structure. As the Lie group captures the symmetries in the data, it is essential from a theoretical and practical perspective to ask for statistical methods that respect these symmetries; this way they are insensitive to confounding effects, e.g., due to the choice of reference coordinate systems. In this work, we investigate geodesic regression -- a generalization of linear regression originally derived for Riemannian manifolds. While Lie groups can be endowed with Riemannian metrics, these are generally incompatible with the group structure. We develop a non-metric estimator using an affine connection setting. It captures geodesic relationships respecting the symmetries given by left and right translations. For its computation, we propose an efficient fixed point algorithm requiring simple differential expressions that can be calculated through automatic differentiation. We perform experiments on a synthetic example and evaluate our method on an open-access, clinical dataset studying knee joint configurations under the progression of osteoarthritis.