Beyond Supervised Classification: Extreme Minimal Supervision with the Graph 1-Laplacian - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2019

Beyond Supervised Classification: Extreme Minimal Supervision with the Graph 1-Laplacian

Angelica I. Aviles-Rivero
  • Fonction : Auteur
Ruoteng Li
  • Fonction : Auteur
  • PersonId : 1051165
Samar M Alsaleh
  • Fonction : Auteur
Robby T Tan
  • Fonction : Auteur

Résumé

We consider the task of classifying when an extremely reduced amount of labelled data is available. This problem is of a great interest, in several real-world problems, as obtaining large amounts of labelled data is expensive and time consuming. We present a novel semi-supervised framework for multi-class classification that is based on the normalised and non-smooth graph 1-Laplacian. Our transductive framework is framed under a novel functional with carefully selected class priors - that enforces a sufficiently smooth solution that strengthens the intrinsic relation between the labelled and unlabelled data. We demonstrate through extensive experimental results on large datasets CIFAR-10 and ChestX-ray14, that our method outperforms classic methods and readily competes with recent deep-learning approaches.
Fichier principal
Vignette du fichier
ems1L.pdf (1.48 Mo) Télécharger le fichier
Vignette du fichier
teaser.png (297.49 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Format Figure, Image
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02170176 , version 1 (22-07-2019)

Identifiants

Citer

Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li, Samar M Alsaleh, Robby T Tan, et al.. Beyond Supervised Classification: Extreme Minimal Supervision with the Graph 1-Laplacian. 2019. ⟨hal-02170176⟩

Collections

CNRS IMB INSMI
98 Consultations
47 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More