Anomaly detection via over-sampling principal component analysis

Yi Ren Yeh*, Zheng Yi Lee, Yuh-Jye Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations


Outlier detection is an important issue in datamining and has been studied in different research areas. It can be used for detecting the small amount of deviated data. In this article, we use "Leave One Out" procedure to check each individual point the "with or without" effect on the variation of principal directions. Based on this idea, an over-sampling principal component analysis outlier detection method is proposed for emphasizing the influence of an abnormal instance (or an outlier). Except for identifying the suspicious outliers, we also design an on-line anomaly detection to detect the new arriving anomaly. In addition, we also study the quick updating of the principal directions for the effective computation and satisfying the on-line detecting demand. Numerical experiments show that our proposed method is effective in computation time and anomaly detection.

Original languageEnglish
Title of host publicationNew Advances in Intelligent Decision Technologies
Subtitle of host publicationResults of the First KES International Symposium IDT 2009
EditorsKazumi Nakamatsu, Gloria Phillips-Wren, Lakhmi Jain, Robert Howlett
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783642009082
StatePublished - 7 May 2009

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

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