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Smoothing graph signals via random spanning forests

Yusuf Pilavci 1 Pierre-Olivier Amblard 1 Simon Barthelme 2 Nicolas Tremblay 1
2 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing
GIPSA-DIS - Département Images et Signal, GIPSA-PSD - GIPSA Pôle Sciences des Données
Abstract : Another facet of the elegant link between random processes on graphs and Laplacian-based numerical linear algebra is uncovered: based on random spanning forests, novel Monte-Carlo estimators for graph signal smoothing are proposed. These random forests are sampled efficiently via a variant of Wilson's algorithm-in time linear in the number of edges. The theoretical variance of the proposed estimators are analyzed , and their application to several problems are considered , such as Tikhonov denoising of graph signals or semi-supervised learning for node classification on graphs.
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https://hal.archives-ouvertes.fr/hal-02319175
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Submitted on : Wednesday, February 5, 2020 - 4:33:17 PM
Last modification on : Tuesday, May 19, 2020 - 11:34:43 AM

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  • HAL Id : hal-02319175, version 2

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Yusuf Pilavci, Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay. Smoothing graph signals via random spanning forests. ICASSP 2020, May 2020, barcelone, Spain. ⟨hal-02319175v2⟩

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