Simultaneous Optimization of Geodesics and Fr\'echet Means

TL;DR

This paper introduces the GEORCE-FM algorithm for efficiently computing the Fréchet mean on Riemannian and Finsler manifolds, demonstrating faster convergence and scalability with theoretical guarantees and empirical improvements.

Contribution

The paper presents a novel simultaneous optimization algorithm for the Fréchet mean and geodesics, extending it to Finsler manifolds and large datasets with proven convergence properties.

Findings

GEORCE-FM converges globally and locally quadratically.

The adaptive extension scales to large data sets.

Empirical results show improved accuracy and runtime over baselines.

Abstract

A central part of geometric statistics is to compute the Fr\'echet mean. This is a well-known intrinsic mean on a Riemannian manifold that minimizes the sum of squared Riemannian distances from the mean point to all other data points. The Fr\'echet mean is simple to define and generalizes the Euclidean mean, but for most manifolds even minimizing the Riemannian distance involves solving an optimization problem. Therefore, numerical computations of the Fr\'echet mean require solving an embedded optimization problem in each iteration. We introduce the GEORCE-FM algorithm to simultaneously compute the Fr\'echet mean and Riemannian distances in each iteration in a local chart, making it faster than previous methods. We extend the algorithm to Finsler manifolds and introduce an adaptive extension such that GEORCE-FM scales to a large number of data points. Theoretically, we show that GEORCE-FM has global convergence and local quadratic convergence and prove that the adaptive extension converges in expectation to the Fr\'echet mean. We further empirically demonstrate that GEORCE-FM outperforms existing baseline methods to estimate the Fr\'echet mean in terms of both accuracy and runtime.