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Publication 18-CNA-028
Properly-Weighted Graph Laplacian For Semi-Supervised Learning Jeff Calder Dejan Slepčev 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 large-sample limit. We prove that our semi-supervised learning algorithm converges, in the infinite sample size limit, to the smooth solution of a continuum variational problem that attains the labeled values continuously. Our method is fast and easy to implement. Get the paper in its entirety as 18-CNA-028.pdf |