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Properly-weighted graph Laplacian for semi-supervised learning
[article]
2019
arXiv
pre-print
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. Several approaches have been proposed recently to address this, however we show that some of them remain ill-posed in the large-data limit. In this paper, we show a way to correctly set the weights in Laplacian regularization so that the estimator remains well posed and stable in the
arXiv:1810.04351v2
fatcat:vjppffhzbfeelhzzluylfiyh5m