By Topic

Semi-Supervised Novelty Detection Using SVM Entire Solution Path

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
de Morsier, F. ; LTS5 Laboratory, École Polytechnique Fédérale de Lausanne , Lausanne, Switzerland ; Tuia, D. ; Borgeaud, M. ; Gass, V.
more authors

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.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:51 ,  Issue: 4 )