-- An approach to estimate the number of rules by spectral analysis of the training dataset has been recently proposed [1]. This work presents an analysis of such a method in high ...
Vinicius da F. Vieira, Alexandre Evsukoff, Beatriz...
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 eigenvecto...
Recently, spectral clustering (a.k.a. normalized graph cut) techniques have become popular for their potential ability at finding irregularlyshaped clusters in data. The input to...
Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-s...
Given a set of n randomly drawn sample points, spectral clustering in its simplest form uses the second eigenvector of the graph Laplacian matrix, constructed on the similarity gra...
Ulrike von Luxburg, Olivier Bousquet, Mikhail Belk...