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Verfasst von:Zisler, Matthias [VerfasserIn]   i
 Zern, Artjom [VerfasserIn]   i
 Boll, Bastian [VerfasserIn]   i
 Petra, Stefania [VerfasserIn]   i
 Schnörr, Christoph [VerfasserIn]   i
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
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