Spectral Clustering Based on the Graph p-Laplacian

author: Thomas Bühler, Department of Computer Science, Saarland University
published: Aug. 26, 2009,   recorded: June 2009,   views: 711
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Description

We present a generalized version of spectral clustering using the graph p-Laplacian, a nonlinear generalization of the standard graph Laplacian. We show that the second eigenvector of the graph p-Laplacian interpolates between a relaxation of the normalized and the Cheeger cut. Moreover, we prove that in the limit as p ! 1 the cut found by thresholding the second eigenvector of the graph p-Laplacian converges to the optimal Cheeger cut. Furthermore, we provide an efficient numerical scheme to compute the second eigenvector of the graph p- Laplacian. The experiments show that the clustering found by p-spectral clustering is at least as good as normal spectral clustering, but often leads to signifi cantly better results.

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Comment2 Professional Assignment Writers - Assignmentspot, May 2, 2019 at 10:22 a.m.:

In this paper our attention lies on the inspiration of phantom bunching as an unwinding of adjusted chart cut criteria. It is notable that the second convectors of the normalized and standardized chart Laplacians compare to relaxations of the proportion cut

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