Feature Extraction for Outlier Detection in High-Dimensional Spaces

Hoang Vu Nguyen, Vivekanand Gopalkrishnan
Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, PMLR 10:66-75, 2010.

Abstract

This work addresses the problem of feature extraction for boosting the performance of outlier detectors in high-dimensional spaces. Recent years have observed the prominence of multidimensional data on which traditional detection techniques usually fail to work as expected due to the curse of dimensionality. This paper introduces an efficient feature extraction method which brings nontrivial improvements in detection accuracy when applied on two popular detection techniques. Experiments carried out on real datasets demonstrate the feasibility of feature extraction in outlier detection.

Cite this Paper

BibTeX
@InProceedings{pmlr-v10-nguyen10a, title = {Feature Extraction for Outlier Detection in High-Dimensional Spaces}, author = {Nguyen, Hoang Vu and Gopalkrishnan, Vivekanand}, booktitle = {Proceedings of the Fourth International Workshop on Feature Selection in Data Mining}, pages = {66--75}, year = {2010}, editor = {Liu, Huan and Motoda, Hiroshi and Setiono, Rudy and Zhao, Zheng}, volume = {10}, series = {Proceedings of Machine Learning Research}, address = {Hyderabad, India}, month = {21 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v10/nguyen10a/nguyen10a.pdf}, url = {https://proceedings.mlr.press/v10/nguyen10a.html}, abstract = {This work addresses the problem of feature extraction for boosting the performance of outlier detectors in high-dimensional spaces. Recent years have observed the prominence of multidimensional data on which traditional detection techniques usually fail to work as expected due to the curse of dimensionality. This paper introduces an efficient feature extraction method which brings nontrivial improvements in detection accuracy when applied on two popular detection techniques. Experiments carried out on real datasets demonstrate the feasibility of feature extraction in outlier detection.} }
Endnote
%0 Conference Paper %T Feature Extraction for Outlier Detection in High-Dimensional Spaces %A Hoang Vu Nguyen %A Vivekanand Gopalkrishnan %B Proceedings of the Fourth International Workshop on Feature Selection in Data Mining %C Proceedings of Machine Learning Research %D 2010 %E Huan Liu %E Hiroshi Motoda %E Rudy Setiono %E Zheng Zhao %F pmlr-v10-nguyen10a %I PMLR %P 66--75 %U https://proceedings.mlr.press/v10/nguyen10a.html %V 10 %X This work addresses the problem of feature extraction for boosting the performance of outlier detectors in high-dimensional spaces. Recent years have observed the prominence of multidimensional data on which traditional detection techniques usually fail to work as expected due to the curse of dimensionality. This paper introduces an efficient feature extraction method which brings nontrivial improvements in detection accuracy when applied on two popular detection techniques. Experiments carried out on real datasets demonstrate the feasibility of feature extraction in outlier detection.
RIS
TY - CPAPER TI - Feature Extraction for Outlier Detection in High-Dimensional Spaces AU - Hoang Vu Nguyen AU - Vivekanand Gopalkrishnan BT - Proceedings of the Fourth International Workshop on Feature Selection in Data Mining DA - 2010/05/26 ED - Huan Liu ED - Hiroshi Motoda ED - Rudy Setiono ED - Zheng Zhao ID - pmlr-v10-nguyen10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 10 SP - 66 EP - 75 L1 - http://proceedings.mlr.press/v10/nguyen10a/nguyen10a.pdf UR - https://proceedings.mlr.press/v10/nguyen10a.html AB - This work addresses the problem of feature extraction for boosting the performance of outlier detectors in high-dimensional spaces. Recent years have observed the prominence of multidimensional data on which traditional detection techniques usually fail to work as expected due to the curse of dimensionality. This paper introduces an efficient feature extraction method which brings nontrivial improvements in detection accuracy when applied on two popular detection techniques. Experiments carried out on real datasets demonstrate the feasibility of feature extraction in outlier detection. ER -
APA
Nguyen, H.V. & Gopalkrishnan, V.. (2010). Feature Extraction for Outlier Detection in High-Dimensional Spaces. Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, in Proceedings of Machine Learning Research 10:66-75 Available from https://proceedings.mlr.press/v10/nguyen10a.html.

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