计算机应用研究2016，Vol.33Issue(5)：1354-1358,5.DOI:10.3969/j.issn.1001-3695.2016.05.017
基于样本选取和加权 KPCA-L1的异常检测
Novelty detection based on sample selection and weighted KPCA-L1
摘要
Abstract
To enhance the speed of L1 norm based KPCA(KPCA-L1 )for tackling novelty detection problems,this paper pro-posed a novelty detection method based on sample selection and weighted KPCA-L1 .For the proposed method,it selected the representative feature subset from the given training set firstly.Furthermore,it signed the samples in the obtained feature subset with weights and used such feature subset to construct the weighted KPCA-L1 .In comparison with KPCA-L1 ,the proposed method can efficiently reduce the size of training set and improve the update way of KPCA-L1 .Experimental results on the syn-thetic and benchmark data sets demonstrate that,compared to KPCA-L1 ,the proposed method can obtain faster modeling speed on the premise of assuming the accuracy rate of novelty detection.关键词
核主成分分析/一范数/样本选取/异常检测Key words
KPCA(kernel principal component analysis)/L1 norm/sample selection/novelty detection分类
信息技术与安全科学引用本文复制引用
安磊磊,邢红杰..基于样本选取和加权 KPCA-L1的异常检测[J].计算机应用研究,2016,33(5):1354-1358,5.基金项目
国家自然科学基金资助项目（60903089，61473111）；河北省自然科学基金资助项目 （）
