Lower bounds on the low-distortion embedding dimension of submanifolds of Rn

Abstract

Let M be a smooth submanifold of Rn equipped with the Euclidean (chordal) metric. This note considers the smallest dimension m for which there exists a bi-Lipschitz function f:M↦Rm with bi-Lipschitz constants close to one. The main result bounds the best achievable embedding dimension m below in terms of the Lipschitz constants of f as well as the reach, volume, diameter, and dimension of M. This new lower bound is then applied to show that prior upper bounds for m via random matrices by Eftekhari and Wakin [6] are near-optimal in a wide range of settings. This supports random linear maps as being nearly as efficient as nonlinear maps at reducing the ambient dimension for manifold data in many situations. Along the way, we also prove results concerning the impossibility of achieving better nonlinear measurement maps with the Restricted Isometry Property (RIP) in compressive sensing applications.