Feature extraction for one-class classification

David M.J. Tax, Klaus R. Müller

Research output: Chapter in Book/Report/Conference proceedingChapter

24 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsOkyay Kaynak, Ethem Alpaydin, Erkki Oja, Lei Xu
PublisherSpringer Verlag
Pages342-349
Number of pages8
ISBN (Print)3540404082, 9783540404088
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2714
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Tax, D. M. J., & Müller, K. R. (2003). Feature extraction for one-class classification. In O. Kaynak, E. Alpaydin, E. Oja, & L. Xu (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 342-349). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2714). Springer Verlag. https://doi.org/10.1007/3-540-44989-2_41