Principal Autoparallel Analysis: Data Analysis in Weitzenböck Space
This addresses the problem of analyzing data on manifolds for researchers in statistics and machine learning, but appears incremental as it adapts existing geometric concepts to a different space.
The paper tackles the challenge of statistical analysis on curved manifolds by transferring the problem to Weitzenböck space, where parallel transport is path-independent, and demonstrates that generating autoparallels in a data-driven fashion yields a useful representation for further analysis.
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 Weitzenböck 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.