Computing Nonlinear Eigenfunctions via Gradient Flow Extinction
Numerical Analysis
2019-02-28 v1 Computer Vision and Pattern Recognition
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.
Cite
@article{arxiv.1902.10414,
title = {Computing Nonlinear Eigenfunctions via Gradient Flow Extinction},
author = {Leon Bungert and Martin Burger and Daniel Tenbrinck},
journal= {arXiv preprint arXiv:1902.10414},
year = {2019}
}
Comments
12 pages, 5 figure, accepted for publication in SSVM conference proceedings 2019