计算机应用研究2016，Vol.33Issue(12)：3539-3542,3564,5.DOI:10.3969/j.issn.1001-3695.2016.12.005
基于再生核希尔伯特空间映射的高维数据特征选择优化算法
Reproducing kernel Hilbert space mapping based feature selection algorithm for high dimensional data
摘要
Abstract
The existing filter feature selection algorithms do not consider the inner structure of nonlinear data,lead to a lower classification accuracy than wrapper feature selection methods.This paper proposed a reproducing kernel Hilbert space map-ping based feature selection algorithm to solve that shortcoming of filter feature selection algorithms.Firstly,it constructed the search tree based on branch and bound method and searched.Then,based on the reproducing kernel Hilbert space mapping, it analyzed the inner structure of nonlinear data.Lastly,based on the inner structure of the data,it selected the optimal dis-tance computing method.Compared simulation experiments results show that the proposal has a similar classification accuracy with wrapper feature selection algorithms,at the same time has obviously better computational efficiency,and can handle the big data analysis.关键词
非线性数据/特征选择/希尔伯特空间/大数据/高维数据Key words
nonlinear data/feature selection/Hilbert space/big data/high dimensional data分类
信息技术与安全科学引用本文复制引用
张静,王树梅..基于再生核希尔伯特空间映射的高维数据特征选择优化算法[J].计算机应用研究,2016,33(12):3539-3542,3564,5.基金项目
国家自然科学基金资助项目（61273076）；江苏省自然科学基金资助项目 （）
