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Found 4 papers, 1 papers with code
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
We propose a new framework, called Poisson learning, for graph based semi-supervised learning at very low label rates.
Properly-weighted graph Laplacian for semi-supervised learning
The performance of traditional graph Laplacian methods for semi-supervised learning degrades substantially as the ratio of labeled to unlabeled data decreases, due to a degeneracy in the graph Laplacian.
Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the Laplace--Beltrami operator
sample from a $m$-dimensional submanifold $M$ in $R^d$ as the sample size $n$ increases and the neighborhood size $h$ tends to zero.
Consistency of Cheeger and Ratio Graph Cuts
We consider point clouds obtained as samples of a ground-truth measure.
