Approximation of symmetric total variation on point clouds

Abstract

The paper investigates the approximation of the symmetric Total Variation functional on graphs. Such an approximation is given in terms of a discrete and symmetric finite difference model defined on point clouds obtained by randomly sampling a reference probability measure. We identify suitable scalings of the point distribution that guarantee an almost surely $\hat{I}“$-convergence to an anisotropic weighted symmetric Total Variation.