Computing nonlinear eigenfunctions via gradient flow extinction
Scale Space and Variational Methods in Computer Vision: 7th International …, 2019•Springer
In this work we investigate the computation of nonlinear eigenfunctions via the extinction
profiles of gradient flows. We analyze a scheme that recursively subtracts such
eigenfunctions from given data and show that this procedure yields a decomposition of the
data into eigenfunctions in some cases as the 1-dimensional total variation, for instance. We
discuss results of numerical experiments in which we use extinction profiles and the gradient
flow for the task of spectral graph clustering as used, eg, in machine learning applications.
profiles of gradient flows. We analyze a scheme that recursively subtracts such
eigenfunctions from given data and show that this procedure yields a decomposition of the
data into eigenfunctions in some cases as the 1-dimensional total variation, for instance. We
discuss results of numerical experiments in which we use extinction profiles and the gradient
flow for the task of spectral graph clustering as used, eg, in machine learning applications.
Abstract
In this work we investigate the computation of nonlinear eigenfunctions via the extinction profiles of gradient flows. We analyze a scheme that recursively subtracts such eigenfunctions from given data and show that this procedure yields a decomposition of the data into eigenfunctions in some cases as the 1-dimensional total variation, for instance. We discuss results of numerical experiments in which we use extinction profiles and the gradient flow for the task of spectral graph clustering as used, e.g., in machine learning applications.
Springer
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