Kernel partial least squares based on least squares support vector machine primal-dual optimization problem
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摘要: 提出了一種基于對偶優化的核最小二乘(KPLS)方法,把KPLS用最小二乘支持向量機的形式表示.推導了KPLS對偶優化形式的公式,且使其具有最小二乘支持向量機的風格.在初始空間中構造優化問題,應用核技術在特征空間中解對偶問題,這種解與非線性的KPLS具有相似性.實驗驗證了這種方法的效果,表明了該方法的有效性和優越性.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.
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