Information theoretic novelty detection

Maurizio Filippone*, Guido Sanguinetti

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

We present a novel approach to online change detection problems when the training sample size is small. The proposed approach is based on estimating the expected information content of a new data point and allows an accurate control of the false positive rate even for small data sets. In the case of the Gaussian distribution, our approach is analytically tractable and closely related to classical statistical tests. We then propose an approximation scheme to extend our approach to the case of the mixture of Gaussians. We evaluate extensively our approach on synthetic data and on three real benchmark data sets. The experimental validation shows that our method maintains a good overall accuracy, but significantly improves the control over the false positive rate.

Original languageEnglish (US)
Pages (from-to)805-814
Number of pages10
JournalPattern Recognition
Volume43
Issue number3
DOIs
StatePublished - Mar 2010

Keywords

  • Density estimation
  • Information theory
  • Mixture of Gaussians
  • Novelty detection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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