高维空间中针对离群点检测的特征抽取

提出了在高维空间中利用特征抽取提高离群点检测性能问题的解决方法。近年来，传统的检测技术已经不能适应高维的数据。介绍了一种有效的基于特征抽取的DROPT方法，该方法整合ERE策略和APCDA方法进行无特征损失的本征空间规则化之后降维，能够大大提高离群点检测精度，在此基础上还可以减小检测难度。实验证明这种在离群点检测中应用特征抽取的方法有一定的实用性。

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,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 can take advantage of both ERE and APCPA which brings nontrivial improvements in detection accuracy in outlier detection.Similar to APCDA,this approach performs engenspace decomposition as well as feature extraction on the weight-adjusted scatter matrices,and applies the strategy of ERE during the eigenspace regularization process to preserve the discriminant information.Experiments carried out on real datasets demonstrate the feasibility of feature extraction in outlier detection.