Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Deep triplet-driven semi-supervised embedding clustering

Abstract : In most real world scenarios, experts dispose of limited background knowledge that they can exploit for guiding the analysis process. In this context, semi-supervised clustering can be employed to leverage such knowledge and enable the discovery of clusters that meet the analysts’ expectations. To this end, we propose a semi-supervised deep embedding clustering algorithm that exploits triplet constraints as background knowledge within the whole learning process. The latter consists in a two-stage approach where, initially, a low-dimensional data embedding is computed and, successively, cluster assignment is refined via the introduction of an auxiliary target distribution. Our algorithm is evaluated on real-world benchmarks in comparison with state-of-the-art unsupervised and semi-supervised clustering methods. Experimental results highlight the quality of the proposed framework as well as the added value of the new learnt data representation.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

Littérature citée [28 références]  Voir  Masquer  Télécharger
Déposant : Dino Ienco <>
Soumis le : dimanche 6 septembre 2020 - 15:33:55
Dernière modification le : jeudi 17 septembre 2020 - 03:35:44
Archivage à long terme le : : mercredi 2 décembre 2020 - 21:10:51


Fichiers produits par l'(les) auteur(s)



Dino Ienco, Ruggero Pensa. Deep triplet-driven semi-supervised embedding clustering. 22nd International Conference on Discovery Science (DS), Oct 2019, Split, Croatia. pp.220-234, ⟨10.1007/978-3-030-33778-0_18⟩. ⟨hal-02931058⟩



Consultations de la notice


Téléchargements de fichiers