高师理科学刊Issue(6)：34-37,4.DOI:10.3969/j.issn.1007-9831.2015.06.011
特征提取算法KPCA的改进与设计
The improvement and design of KPCA feature extraction algorithm
摘要
Abstract
Transformation is the essence of the feature extraction.In view of the traditional kernel principal component analysis(KPCA)lack of extracted feature combination in classification problems,proposes an improved KPCA based on information measure.Data set uses the normative KDDCUP99 security audit data set.The aggregation degree within class and dispersion degree between class comprise information measure of each feature vector in the training sample.It is used to replace the cumulative contribution rate of the traditional KPCA.The selected feature combination is advantageous to classification.A large amount of experimental results show that the improved KPCA method at a lower dimension will have a more pronounced effect of classification.关键词
KPCA/特征提取/分类Key words
KPCA/feature extraction/classification分类
信息技术与安全科学引用本文复制引用
何新,李大辉,付军..特征提取算法KPCA的改进与设计[J].高师理科学刊,2015,(6):34-37,4.基金项目
黑龙江省自然科学基金项目 （）
