Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines
Details
State: Public
Version: author
Serval ID
serval:BIB_03B24818EBC8
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines
Title of the conference
IGARSS 2012, IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Address
Munich, Germany, July 22-27, 2012
ISBN
978-1-4673-1159-5
Publication state
Published
Issued date
2012
Volume
2012
Series
IEEE International Symposium on Geoscience and Remote Sensing IGARSS
Pages
7337-7340
Language
english
Abstract
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.
Keywords
Novelty detection, Support Vector Machines, , Regularization path, Semi-Supervised, Unsupervised, , Nested, LTS5
Create date
06/01/2014 22:07
Last modification date
20/08/2019 13:25
Usage data
