The high dimensionality and massive amount of data in open environments make the existing low-dimensional outlier detection methods time-consuming. support vector machine (SVM) is a commonly used method. However, SVM still faces problem difficulty obtaining optimal parameters quickly effectively, resulting low efficiency, poor stability, applying to environment datasets. In order improve effici...

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 im...