Semi-Supervised Novelty Detection Using SVM Entire Solution Path

Details

Ressource 1Download: BIB_24E4728194E0.P001.pdf (7780.15 [Ko])
State: Public
Version: author
Serval ID
serval:BIB_24E4728194E0
Type
Article: article from journal or magazin.
Collection
Publications
Title
Semi-Supervised Novelty Detection Using SVM Entire Solution Path
Journal
IEEE Transactions on Geoscience and Remote Sensing
Author(s)
de Morsier F., Tuia D., Borgeaud M., Gass V., Thiran J.P.
ISSN
0196-2892
Publication state
Published
Issued date
2013
Volume
51
Number
4
Pages
1939-1950
Language
english
Abstract
Very often, the only reliable information available to perform change detection is the description of some "unchanged" regions. Since, sometimes, these regions do not contain all the relevant information to identify their counterpart (the changes), we consider the use of unlabeled data to perform semi-supervised novelty detection (SSND). SSND can be seen as an unbalanced classification problem solved using the cost-sensitive support vector machine (CS-SVM), but this requires a heavy parameter search. Here, we propose the use of entire solution path algorithms for the CS-SVM in order to facilitate and accelerate parameter selection for SSND. Two algorithms are considered and evaluated. The first algorithm is an extension of the CS-SVM algorithm that returns the entire solution path in a single optimization. This way, optimization of a separate model for each hyperparameter set is avoided. The second algorithm forces the solution to be coherent through the solution path, thus producing classification boundaries that are nested (included in each other). We also present a low-density (LD) criterion for selecting optimal classification boundaries, thus avoiding recourse to cross validation (CV) that usually requires information about the "change" class. Experiments are performed on two multitemporal change detection data sets (flood and fire detection). Both algorithms tracing the solution path provide similar performances than the standard CS-SVM while being significantly faster. The proposed LD criterion achieves results that are close to the ones obtained by CV but without using information about the changes.
Web of science
Create date
06/01/2014 17:48
Last modification date
20/08/2019 13:03
Usage data