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Asymptotic behavior of \(\ell_p\)-based Laplacian regularization in semi-supervised learning

2016
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Annual Conference Computational Learning Theory
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Given a weighted graph with N vertices, consider a real-valued regression problem in a semisupervised setting, where one observes n labeled vertices, and the task is to label the remaining ones. We present a theoretical study of p -based Laplacian regularization under a d-dimensional geometric random graph model. We provide a variational characterization of the performance of this regularized learner as N grows to infinity while n stays constant; the associated optimality conditions lead to a

dblp:conf/colt/Alaoui16
fatcat:m2muony7czauvcpcdfv3ewa6qy