Improved graph Laplacian via geometric self-consistencyDownload PDFOpen Website

2014 (modified: 08 Nov 2022)CoRR 2014Readers: Everyone
Abstract: We address the problem of setting the kernel bandwidth used by Manifold Learning algorithms to construct the graph Laplacian. Exploiting the connection between manifold geometry, represented by the Riemannian metric, and the Laplace-Beltrami operator, we set the bandwidth by optimizing the Laplacian's ability to preserve the geometry of the data. Experiments show that this principled approach is effective and robust.
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