Convergence Rate of the Symmetrically Normalized Graph Laplacian

preprint2011arXivOpen access

Abstract

This short note aims at (re)proving that the symmetrically normalized graph Laplacian $L=\Id - D^{-1/2}WD^{-1/2}$ (from a graph defined from a Gaussian weighting kernel on a sampled smooth manifold) converges towards the continuous Manifold Laplacian when the sampling become infinitely dense. The convergence rate with respect to the number of samples NN is O(1/N)O(1/N).

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