29 citations · 46 across the 6 of their papers we have counts for
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stat.ML2018
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms
Matthew M. Dunlop, Dejan Slepčev, Andrew M. Stuart +1
Scalings in which the graph Laplacian approaches a differential operator in the large graph limit are used to develop understanding of a number of algorithms for semi-supervised le…
stat.ML2018★ 12 cited
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…