Geometric PDEs on weighted graphs for semi-supervised classification
Résumé
In this paper, we consider the adaptation of two Partial Differential Equations (PDEs) on weighted graphs, p-Laplacian and eikonal equations, for semi-supervised classifi-cation tasks. These equations are a discrete analogue of well known geometric PDEs, which are widely used in image pro-cessing. While the p-Laplacian on graphs was intensively used in data classification, few works relate to the eikonal equation for data classification. The methods are illustrated through semi-supervised classification tasks on databases, where we compare the two algorithms. The results show that these methods perform well regarding the state-of-the-art and are applicable to the task of semi-supervised classification.
Domaines
Traitement des images [eess.IV]
Origine : Fichiers produits par l'(les) auteur(s)
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