Bungert L, Burger M, Tenbrinck D (2019)
Publication Language: English
Publication Status: Accepted
Publication Type: Book chapter / Article in edited volumes
Future Publication Type: Conference contribution
Publication year: 2019
Publisher: Springer Verlag
Edited Volumes: Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings
Series: Lecture Notes in Computer Science
Pages Range: 291-302
Event location: Hofgeismar
ISBN: 978-3-030-22367-0
URI: https://arxiv.org/abs/1902.10414
DOI: 10.1007/978-3-030-22368-7_23
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.
APA:
Bungert, L., Burger, M., & Tenbrinck, D. (2019). Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. In Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings. (pp. 291-302). Springer Verlag.
MLA:
Bungert, Leon, Martin Burger, and Daniel Tenbrinck. "Computing Nonlinear Eigenfunctions via Gradient Flow Extinction." Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings. Springer Verlag, 2019. 291-302.
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