Semi-supervised novelty detection using SVM entire solution path

Frank De Morsier*, Devis Tuia, Maurice Borgeaud, Volker Gass, Jean Philippe Thiran

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

29 Citations (Scopus)


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.

Original languageEnglish
Article number6461095
Pages (from-to)1939-1950
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number4
Publication statusPublished - 15 Feb 2013
Externally publishedYes


  • Change detection
  • cost-sensitive support vector machine (CS-SVM)
  • learning from positive and unlabeled examples
  • low-density (LD) separation
  • nested support vector machine (SVM)
  • unsupervised parameter selection


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