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Graph-based semi-supervised learning: A comprehensive review
Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of
both labeled and unlabelled data. An essential class of SSL methods, referred to as graph …
both labeled and unlabelled data. An essential class of SSL methods, referred to as graph …
Contrastive and generative graph convolutional networks for graph-based semi-supervised learning
Abstract Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a
handful of labeled data to the remaining massive unlabeled data via a graph. As one of the …
handful of labeled data to the remaining massive unlabeled data via a graph. As one of the …
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. Poisson learning is motivated by the need to address the …
learning at very low label rates. Poisson learning is motivated by the need to address the …
Improved spectral convergence rates for graph Laplacians on ε-graphs and k-NN graphs
In this paper we improve the spectral convergence rates for graph-based approximations of
weighted Laplace-Beltrami operators constructed from random data. We utilize regularity of …
weighted Laplace-Beltrami operators constructed from random data. We utilize regularity of …
Consistency of Lipschitz learning with infinite unlabeled data and finite labeled data
J Calder - SIAM Journal on Mathematics of Data Science, 2019 - SIAM
We study the consistency of Lipschitz learning on graphs in the limit of infinite unlabeled
data and finite labeled data. Previous work has conjectured that Lipschitz learning is well …
data and finite labeled data. Previous work has conjectured that Lipschitz learning is well …
Graph-based active learning for semi-supervised classification of SAR data
We present a novel method for classification of Synthetic Aperture Radar (SAR) data by
combining ideas from graph-based learning and neural network methods within an active …
combining ideas from graph-based learning and neural network methods within an active …
Lipschitz regularity of graph Laplacians on random data clouds
In this paper we study Lipschitz regularity of elliptic PDEs on geometric graphs, constructed
from random data points. The data points are sampled from a distribution supported on a …
from random data points. The data points are sampled from a distribution supported on a …
Uniform convergence rates for Lipschitz learning on graphs
Lipschitz learning is a graph-based semisupervised learning method where one extends
labels from a labeled to an unlabeled data set by solving the infinity Laplace equation on a …
labels from a labeled to an unlabeled data set by solving the infinity Laplace equation on a …
Rates of convergence for Laplacian semi-supervised learning with low labeling rates
We investigate graph-based Laplacian semi-supervised learning at low labeling rates (ratios
of labeled to total number of data points) and establish a threshold for the learning to be well …
of labeled to total number of data points) and establish a threshold for the learning to be well …
Hamilton-Jacobi equations on graphs with applications to semi-supervised learning and data depth
Shortest path graph distances are widely used in data science and machine learning, since
they can approximate the underlying geodesic distance on the data manifold. However, the …
they can approximate the underlying geodesic distance on the data manifold. However, the …