Web: http://arxiv.org/abs/1901.05031

Jan. 24, 2022, 2:10 a.m. | Mauricio Flores Rios, Jeff Calder, Gilad Lerman

cs.LG updates on arXiv.org arxiv.org

This paper addresses theory and applications of $\ell_p$-based Laplacian
regularization in semi-supervised learning. The graph $p$-Laplacian for $p>2$
has been proposed recently as a replacement for the standard ($p=2$) graph
Laplacian in semi-supervised learning problems with very few labels, where
Laplacian learning is degenerate.

In the first part of the paper we prove new discrete to continuum convergence
results for $p$-Laplace problems on $k$-nearest neighbor ($k$-NN) graphs, which
are more commonly used in practice than random geometric graphs. Our analysis …

algorithms analysis arxiv graphs learning math semi-supervised learning supervised learning

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