29 citations · 45 across the 3 of their papers we have counts for
5 papers
Rates of Convergence for Laplacian Semi-Supervised Learning with Low Labeling Rates
Jeff Calder, Dejan Slepčev, Matthew Thorpe
We study graph-based Laplacian semi-supervised learning at low labeling rates. Laplacian learning uses harmonic extension on a graph to propagate labels. At very low label rates, L…
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Jeff Calder, Brendan Cook, Matthew Thorpe +1
We propose a new framework, called Poisson learning, for graph based semi-supervised learning at very low label rates. Poisson learning is motivated by the need to address the dege…
Mumford-Shah functionals on graphs and their asymptotics
Marco Caroccia, Antonin Chambolle, Dejan Slepčev
We consider adaptations of the Mumford-Shah functional to graphs. These are based on discretizations of nonlocal approximations to the Mumford-Shah functional. Motivated by applica…
Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the Laplace--Beltrami operator
Nicolas Garcia Trillos, Moritz Gerlach, Matthias Hein +1
We study the convergence of the graph Laplacian of a random geometric graph generated by an i.i.d. sample from a -dimensional submanifold in as the sample size inc…
Analysis of -Laplacian Regularization in Semi-Supervised Learning
Dejan Slepčev, Matthew Thorpe
We investigate a family of regression problems in a semi-supervised setting. The task is to assign real-valued labels to a set of sample points, provided a small training subse…