he 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 ɛ = ɛ_n, where n is the number of data points. We study the large data asymptotics, i.e. as n → ∝, in the regime where ɛ_n → 0. The mathematical tool used to address this question is Γ-convergence. It is proved that the discrete model converges to a weighted anisotropic perimeter.
|Journal||European Journal of Applied Mathematics|
|Publication status||Published - 14 Nov 2018|