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

GUO Hui; LIU Heping; WANG Ling

doi:10.13374/j.issn1001-053x.2006.08.019

Volume 28 Issue 8

Aug. 2021

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Engineering > 2006 > 28(8): 790-793

GUO Hui, LIU Heping, WANG Ling. Kernel partial least squares based on least squares support vector machine primal-dual optimization problem[J]. Chinese Journal of Engineering, 2006, 28(8): 790-793. doi: 10.13374/j.issn1001-053x.2006.08.019

Citation:

GUO Hui, LIU Heping, WANG Ling. Kernel partial least squares based on least squares support vector machine primal-dual optimization problem[J]. Chinese Journal of Engineering, 2006, 28(8): 790-793. doi: 10.13374/j.issn1001-053x.2006.08.019

Citation:

PDF( 186 KB)

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

doi: 10.13374/j.issn1001-053x.2006.08.019

Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China

Received Date: 2005-04-25
Rev Recd Date: 2005-09-22

Available Online: 2021-08-24

Abstract

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.