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Generalized Median Graph via Iterative Alternate Minimizations

Abstract : Computing a graph prototype may constitute a core element for clustering or classification tasks. However, its computation is an NP-Hard problem, even for simple classes of graphs. In this paper, we propose an efficient approach based on block coordinate descent to compute a generalized median graph from a set of graphs. This approach relies on a clear definition of the optimization process and handles labeling on both edges and nodes. This iterative process optimizes the edit operations to perform on a graph alternatively on nodes and edges. Several experiments on different datasets show the efficiency of our approach.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-02162838
Contributor : Luc Brun <>
Submitted on : Monday, June 24, 2019 - 12:55:33 PM
Last modification on : Thursday, March 5, 2020 - 10:48:02 AM

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  • HAL Id : hal-02162838, version 1
  • ARXIV : 1906.11009

Citation

Nicolas Boria, Sébastien Bougleux, Benoit Gaüzère, Luc Brun. Generalized Median Graph via Iterative Alternate Minimizations. IAPR International workshop on Graph-Based Representation in Pattern Recognition, Donatello Conte, Jean-Yves Ramel,, Jun 2019, Tours, France. pp.99-109. ⟨hal-02162838⟩

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