Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines
Détails
Etat: Public
Version: de l'auteur⸱e
ID Serval
serval:BIB_03B24818EBC8
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines
Titre de la conférence
IGARSS 2012, IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Adresse
Munich, Germany, July 22-27, 2012
ISBN
978-1-4673-1159-5
Statut éditorial
Publié
Date de publication
2012
Volume
2012
Série
IEEE International Symposium on Geoscience and Remote Sensing IGARSS
Pages
7337-7340
Langue
anglais
Résumé
Very often in change detection only few labels or even
none are available. In order to perform change detection
in these extreme scenarios, they can be considered as
novelty detection problems, semi-supervised (SSND) if
some labels are available otherwise unsupervised (UND).
SSND can be seen as an unbalanced classification between
labeled and unlabeled samples using the Cost-Sensitive
Support Vector Machine (CS-SVM). UND assumes novelties in
low density regions and can be approached using the
One-Class SVM (OC-SVM). We propose here to use nested
entire solution path algorithms for the OC-SVM and CS-SVM
in order to accelerate the parameter selection and
alleviate the dependency to labeled ``changed'' samples.
Experiments are performed on two multitemporal change
detection datasets (flood and fire detection) and the
performance of the two methods proposed compared.
none are available. In order to perform change detection
in these extreme scenarios, they can be considered as
novelty detection problems, semi-supervised (SSND) if
some labels are available otherwise unsupervised (UND).
SSND can be seen as an unbalanced classification between
labeled and unlabeled samples using the Cost-Sensitive
Support Vector Machine (CS-SVM). UND assumes novelties in
low density regions and can be approached using the
One-Class SVM (OC-SVM). We propose here to use nested
entire solution path algorithms for the OC-SVM and CS-SVM
in order to accelerate the parameter selection and
alleviate the dependency to labeled ``changed'' samples.
Experiments are performed on two multitemporal change
detection datasets (flood and fire detection) and the
performance of the two methods proposed compared.
Mots-clé
Novelty detection, Support Vector Machines, , Regularization path, Semi-Supervised, Unsupervised, , Nested, LTS5
Création de la notice
06/01/2014 22:07
Dernière modification de la notice
20/08/2019 13:25
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