基于最小二乘支持向量机对偶优化问题的核偏最小二乘  

Kernel partial least squares based on least squares support vector machine primal-dual optimization problem

作　　者：郭辉[1] 刘贺平[1] 王玲[1] 

机构地区：[1]北京科技大学信息工程学院,北京100083

出　　处：《北京科技大学学报》2006年第8期790-793,共4页Journal of University of Science and Technology Beijing

基　　金：国家"863计划"资助项目(No.2002AA412010-10);国家科技部攻关项目(No.2003EG113016)

摘　　要：提出了一种基于对偶优化的核最小二乘(KPLS)方法,把KPLS用最小二乘支持向量机的形式表示.推导了KPLS对偶优化形式的公式,且使其具有最小二乘支持向量机的风格.在初始空间中构造优化问题,应用核技术在特征空间中解对偶问题,这种解与非线性的KPLS具有相似性.实验验证了这种方法的效果,表明了该方法的有效性和优越性.A kernel partial least squares （KPLS） method based on dual optimization was proposed,which was expressed by least squares support vector machine. The KPLS formulae in the form of dual opti-mization were deduced, which had the style of least squares support vector machine. The optimization problem was constructed in a prime space, the dual problem was solved in a eigenspace by the kernel skill and the solutions were the same as nonlinear KPLS.

关 键 词：优化问题 偏最小二乘 最小二乘支持向量机 核偏最小二乘 

分 类 号：TP301.5[自动化与计算机技术—计算机系统结构]