Principal subbundles for dimension reduction

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, denoted principal subbundles. In particular, we compute local approximations by local PCAs and collect them into a rank \textit{k} tangent subbundle on $\mathbb{R}^d$, $k<d$. This determines a sub-Riemannian metric on $\R^d$. We show that sub-Riemannian geodesics w.r.t. 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 $\R^k$, 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.</p>