Feature Extraction for Outlier Detection in High-Dimensional Spaces

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.