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

作　　者：张静[1] 王树梅[1] Zhang Jing Wang Shumei(School of Computer Science ＆ Technology, Nanjing University of Science ＆ Technology, Nanjing 210014, China)

机构地区：[1]南京理工大学计算机科学与技术学院,南京210014

出　　处：《计算机应用研究》2016年第12期3539-3542,3564,共5页Application Research of Computers

基　　金：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(BK20141403)

摘　　要：现有过滤型特征选择算法并未考虑非线性数据的内在结构,从而分类准确率远远低于封装型算法,对此提出一种基于再生核希尔伯特空间映射的高维数据特征选择算法。首先基于分支定界法建立搜索树,并对其进行搜索;然后基于再生核希尔伯特空间映射分析非线性数据的内部结构;最后根据数据集的内部结构选择最优的距离计算方法。对比仿真实验结果表明,该方法与封装型特征选择算法具有接近的分类准确率,同时在计算效率上具有明显的优势,适用于大数据分析。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 mapping 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 distance 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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]