空军工程大学学报（自然科学版）Issue(6)：68-72,5.DOI:10.3969/j.issn.1009-3516.2013.06.016
一种 CDRWPCA 网络故障特征提取算法
A Center Distance Ration Weighted Principal Component Analysis Algorithm for Network Fault Feature Extraction
摘要
Abstract
In view of the problem that the useful classification information in principal component analysis (PCA)may be lost in the process of network fault feature extraction,a new method named center distance ration weighted principal component analysis(CDRWPCA).According to sample category information,the center distance ratio of the difference between characteristics is measured by using this algorithm.By doing so,the weight is designed based on the feature discrimination.Then the weighted datasets are used for PCA feature extraction.Finally,the extracted datasets are sent to support vector machines (SVM)so as to verify the effectiveness of the algorithm.Experiments on network fault diagnosis demonstrate that the the proposed algorithm can improve the compression ratio and the final fault recognition rate.关键词
特征提取/主元成分分析/中心距离比值Key words
feature extraction/principal component analysis/center distance ratio分类
信息技术与安全科学引用本文复制引用
杨婷,孟相如,温祥西,刘青原..一种 CDRWPCA 网络故障特征提取算法[J].空军工程大学学报（自然科学版）,2013,(6):68-72,5.基金项目
国家自然科学基金资助项目(61201209) （61201209）
