On the estimation of geodesic paths on sampled manifolds under random projections

In this paper, we focus on the use of random projections as a dimensionality reduction tool for sampled manifolds in high- dimensional Euclidean spaces. We show that geodesic paths approximations from nearest neighbors Euclidean distances are well-preserved by Gaussian projections and we characterize the distribution of geodesic lengths in the reduced dimensional point cloud. A stylized application to a real-world data set of human faces is presented to validate our theoretical findings.