Filippone, M. and Sanguinetti, G. (2010) Information theoretic novelty detection. Pattern Recognition, 43(3), pp. 805-814. (doi: 10.1016/j.patcog.2009.07.002)
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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.
Item Type: | Articles |
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Additional Information: | NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 43(3), 2010, DOI: 10.1016/j.patcog.2009.07.002 |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Filippone, Dr Maurizio |
Authors: | Filippone, M., and Sanguinetti, G. |
College/School: | College of Science and Engineering > School of Computing Science |
Journal Name: | Pattern Recognition |
Publisher: | Elsevier |
ISSN: | 0031-3203 |
Published Online: | 15 July 2009 |
Copyright Holders: | Copyright © 2010 Elsevier |
First Published: | First published in Pattern Recognition 43(3):805-814 |
Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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