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An Online Support Vector Machine for Abnormal Events Detection




The ability to detect online abnormal events in signals is essential in many real-world Signal Processing applications. Previous algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. Corresponding implementation relies on maximum likelihood or on Bayes estimation theory with generally excellent performance. However, there are numerous cases where a robust and tractable model cannot be obtained, and model-free approaches need to be considered. In this paper, we investigate a machine learning, descriptor-based approach that does not require an explicit descriptors statistical model, based on Support Vector novelty detection. A sequential optimization algorithm is introduced. Theoretical considerations as well as simulations on real signals demonstrate its practical efficiency.

Author(s): Davy, M. and Desobry, F. and Gretton, A. and Doncarli, C.
Journal: Signal Processing
Volume: 86
Number (issue): 8
Pages: 2009-2025
Year: 2006
Month: August
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1016/j.sigpro.2005.09.027
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {An Online Support Vector Machine for Abnormal Events Detection},
  author = {Davy, M. and Desobry, F. and Gretton, A. and Doncarli, C.},
  journal = {Signal Processing},
  volume = {86},
  number = {8},
  pages = {2009-2025},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2006},
  month_numeric = {8}