  We introduce the Wasserstein transform, a method for enhancing and denoising
datasets defined on general metric spaces. The construction draws inspiration
from Optimal Transportation ideas. We establish precise connections with the
mean shift family of algorithms and establish the stability of both our method
and mean shift under data perturbation.
