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Feature extraction for one-class classification

: Tax, D.M.J.; Müller, K.-R.

Kaynak, O.:
Artificial neural networks and neural information processing : Joint international conference, Istanbul, Turkey, June 26 - 29, 2003. Proceedings; ICANN/ICONIP 2003
Berlin: Springer, 2003 (Lecture Notes in Computer Science 2714)
ISBN: 3-540-40408-2
International Conference on Artificial Neural Networks (ICANN) <2003, Istanbul>
International Conference on Neural Information Processing (ICONIP) <2003, Istanbul>
Conference Paper
Fraunhofer FIRST ()

Feature reduction is often an essential part of solving a classification task. One common approach for doing this, is Principal Component Analysis. There the low variance directions in the data are removed and the high variance directions are retained. It is hoped that these high variance directions contain information about the class differences. For one-class classification or novelty detection, the classification task contains one ill-determined class, for which (almost) no information is available. In this paper we show that for one-class classification, the low-variance directions are most informative, and that in the feature reduction a bias-variance trade-off has to be considered which causes that retaining the high variance directions is often not optimal.