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基于支持向量机优化参数的集成多核典型相关分析
引用本文:路燕,盛姝.基于支持向量机优化参数的集成多核典型相关分析[J].科技管理研究,2018(15).
作者姓名:路燕  盛姝
作者单位:山东科技大学
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
摘    要:提出一种可有效处理多个数据集合之间变量关系的集成多核典型相关分析方法,构造一个特殊的核函数,使其更好地将原始样本数据映射到高维空间;基于支持向量机,在选择一个优化参数的基础上最大化多组数据集变量间的关系,以寻求整体相关性最大。在多特征手写体数字库上的实验证明,相比传统的典型相关分析与核典型相关分析方法,基于优化参数的集成多核典型相关分析方法具有更优的性能。

关 键 词:优化核参数  集成多核典型相关分析法  支持向量机  惩罚因子
收稿时间:2017/10/16 0:00:00
修稿时间:2018/7/30 0:00:00

Integrated Multi-Kernel Canonical Correlation Analysis Based on the optimization parameters of SVM
Abstract:Due to its efficient addressing of non-linear relationship among variables which cannot be extracted by traditional canonical correlation analysis (CCA), kernel canonical correlation analysis (KCCA) has attracted increasing attention. This paper presents an integrated multi-kernel canonical correlation analysis (IMKCCA) method that can efficiently deal with the relationship among variables in multiple data sets. In this method, a special kernel function was constructed, which helped to project the original sample data onto a high-dimensional space; in addition, optimization parameters of SVM was selected to maximize the relationships among multiple groups of data set. Tests on Multiple Feature Data Set illustrated that compared with the traditional CCA and KCCA, IMKCCA based on the optimum parameters of SVM has better performance.
Keywords:optimization kernel parameter  IMCCA  SVM  penalty factor
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