| Online-Ressource |
Verfasst von: | Zisler, Matthias [VerfasserIn]  |
| Zern, Artjom [VerfasserIn]  |
| Boll, Bastian [VerfasserIn]  |
| Petra, Stefania [VerfasserIn]  |
| Schnörr, Christoph [VerfasserIn]  |
Titel: | Unsupervised data labeling on graphs by self-assignment flows |
Verf.angabe: | Matthias Zisler, Artjom Zern, Bastian Boll, Stefania Petra, and Christoph Schnörr |
Jahr: | 2021 |
Umfang: | 2 S. |
Fussnoten: | First published: 25 January 2021 ; Gesehen am 18.09.2021 |
Titel Quelle: | Enthalten in: Proceedings in applied mathematics and mechanics |
Ort Quelle: | Weinheim [u.a.] : Wiley-VCH, 2002 |
Jahr Quelle: | 2021 |
Band/Heft Quelle: | 20(2021), 1, Artikel-ID e202000156, Seite 1-2 |
ISSN Quelle: | 1617-7061 |
Abstract: | This paper extends the recently introduced assignment flow approach for supervised image labeling to unsupervised scenarios where no labels are given. The resulting self-assignment flow takes a pairwise data affinity matrix as input data and maximizes the correlation with a low-rank matrix that is parametrized by the variables of the assignment flow, which entails an assignment of the data to themselves through the formation of latent labels (feature prototypes). A single user parameter, the neighborhood size for the geometric regularization of assignments, drives the entire process. By smooth geodesic interpolation between different normalizations of self-assignment matrices on the positive definite matrix manifold, a one-parameter family of self-assignment flows is defined. Accordingly, our approach can be characterized from different viewpoints, e.g. as performing spatially regularized, rank-constrained discrete optimal transport, or as computing spatially regularized normalized spectral cuts. Regarding combinatorial optimization, our approach successfully determines completely positive factorizations of self-assignments in large-scale scenarios, subject to spatial regularization. Various experiments including the unsupervised learning of patch dictionaries using a locally invariant distance function, illustrate the properties of the approach. |
DOI: | doi:10.1002/pamm.202000156 |
URL: | kostenfrei: Volltext ; Verlag: https://doi.org/10.1002/pamm.202000156 |
| kostenfrei: Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/pamm.202000156 |
| DOI: https://doi.org/10.1002/pamm.202000156 |
Datenträger: | Online-Ressource |
Sprache: | eng |
K10plus-PPN: | 1770926712 |
Verknüpfungen: | → Zeitschrift |
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Lokale URL UB: | Zum Volltext |
Unsupervised data labeling on graphs by self-assignment flows / Zisler, Matthias [VerfasserIn]; 2021 (Online-Ressource)