Principal Autoparallel Analysis: Data Analysis in Weitzenb\"{o}ck Space

TL;DR

This paper introduces a novel data analysis method in Weitzenböck space, leveraging autoparallels for more effective analysis of data on manifolds with torsion but no curvature.

Contribution

It proposes a new approach to analyze manifold data by transferring the problem to Weitzenböck space and utilizing autoparallels, which are path-independent and more natural for data representation.

Findings

Autoparallels can be generated data-driven.

Representation in Weitzenböck space improves analysis.

Method handles curvature-induced challenges in manifold data.

Abstract

The statistical analysis of data lying on a differentiable, locally Euclidean, manifold introduces a variety of challenges because the analogous measures to standard Euclidean statistics are local, that is only defined within a neighbourhood of each datapoint. This is because the curvature of the space means that the connection of Riemannian geometry is path dependent. In this paper we transfer the problem to Weitzenb\"{o}ck space, which has torsion, but not curvature, meaning that parallel transport is path independent, and rather than considering geodesics, it is natural to consider autoparallels, which are `straight' in the sense that they follow the local basis vectors. We demonstrate how to generate these autoparallels in a data-driven fashion, and show that the resulting representation of the data is a useful space in which to perform further analysis.