Large data limit for a phase transition model with the p -Laplacian on point clouds

The consistency of a non-local anisotropic Ginzburg-Landau type functional for data classification and clustering is studied. The Ginzburg-Landau objective functional combines a double well potential, that favours indicator valued functions, and the p-Laplacian, that enforces regularity. Under appropriate scaling between the two terms, minimisers exhibit a phase transition on the order of = _n, where n is the number of data points. We study the large data asymptotics, i.e. as n, in the regime where _n 0. The mathematical tool used to address this question is "-convergence. It is proved that the discrete model converges to a weighted anisotropic perimeter.