New paper on novelty detection by Frank de Morsier et al.
© 2012 EPFL
The new paper entitled "Semi-Supervised Novelty Detection using SVM entire solution path" has been accepted for publication in the IEEE Transactions on Geoscience and Remote Sensing in the "Analysis of Multitemporal Remote Sensing Data" Special Issue.
Semi-Supervised Novelty Detection using SVM entire solution path
F. de Morsier, D. Tuia, M. Borgeaud, V. Gass & J.-P. Thiran
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. We propose here to use entire solution path algorithms for the CS-SVM in order to facilitate and accelerate the parameter selection for SSND. We also present a low density criterion for selecting the optimal classification boundaries, thus avoiding the recourse to cross-validation that usually requires information about the “change” class. Experiments are performed on two multitemporal change detection datasets (flood and fire detection) and show equivalent accuracy for much lower computational costs.
