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

dc.contributor.authorAVILES-RIVERO, Angelica I.
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
dc.contributor.authorLI, Ruoteng
dc.contributor.authorALSALEH, Samar M
dc.contributor.authorTAN, Robby T
hal.structure.identifierDepartment of Applied Mathematics and Theoretical Physics [DAMTP]
dc.contributor.authorSCHÖNLIEB, Carola-Bibiane
dc.date.accessioned2024-04-04T03:00:20Z
dc.date.available2024-04-04T03:00:20Z
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192809
dc.description.abstractEnWe 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.
dc.language.isoen
dc.title.enBeyond Supervised Classification: Extreme Minimal Supervision with the Graph 1-Laplacian
dc.typeDocument de travail - Pré-publication
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.identifier.arxiv1906.08635
dc.description.sponsorshipEuropeNonlocal Methods for Arbitrary Data Sources
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-02170176
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02170176v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=AVILES-RIVERO,%20Angelica%20I.&PAPADAKIS,%20Nicolas&LI,%20Ruoteng&ALSALEH,%20Samar%20M&TAN,%20Robby%20T&rft.genre=preprint


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