Principal subbundles for dimension reduction

TL;DR

This paper introduces principal subbundles derived from local PCA approximations to enable manifold learning, surface reconstruction, and distance computation using sub-Riemannian geometry, demonstrating robustness and theoretical guarantees.

Contribution

It proposes a novel framework combining local PCA with sub-Riemannian geometry for manifold learning and surface reconstruction, with proven convergence and robustness.

Findings

Successful application to manifold reconstruction and point-cloud representation.

Robustness to noisy data demonstrated through simulations.

Framework generalizes to known Riemannian manifolds.

Abstract

In this paper we demonstrate how sub-Riemannian geometry can be used for manifold learning and surface reconstruction by combining local linear approximations of a point cloud to obtain lower dimensional bundles. Local approximations obtained by local PCAs are collected into a rank $k$ tangent subbundle on $\mathbb{R}^d$, $k<d$, which we call a principal subbundle. This determines a sub-Riemannian metric on $\mathbb{R}^d$. We show that sub-Riemannian geodesics with respect to this metric can successfully be applied to a number of important problems, such as: explicit construction of an approximating submanifold $M$, construction of a representation of the point-cloud in $\mathbb{R}^k$, and computation of distances between observations, taking the learned geometry into account. The reconstruction is guaranteed to equal the true submanifold in the limit case where tangent spaces are estimated exactly. Via simulations, we show that the framework is robust when applied to noisy data. Furthermore, the framework generalizes to observations on an a priori known Riemannian manifold.