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

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.

Abstract: 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.The model was illustrated with some examples. The results show that the proposed method is effective and superior.