The reverse logistics evaluation based on KPCA-LINMAP model

被引：0

作者：

Zhang, Cai-Qing ^{[1
]}

Lu, Yan-Chao ^{[1
]}

机构：

[1] North China Elect Power Univ, Dept Econ Management, Baoding 071003, Peoples R China

来源：

PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年

关键词：

reverse logistics evaluation; principal components analysis; kernel function; the coupling model of LINMAP;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

According to the limitation of Principal Components Analysis (PCA) in dealing with the nonlinear data, connecting with the Linear Programming Techniques for Multidimensional Analysis of Preference (LINMAP), this paper presents the Kernel Principal Components Analysis-Linear Programming Techniques for Multidimensional Analysis of Preference (KPCA-LINMAP) evaluation model. In addition, the weight of each index can be obtained in this model, thus it makes up another shortage of PCA. In reverse logistics evaluation, the indexes are numerous and the degree of correlation is not high, the model is fitter for this situation than the traditional PCA. At last, the validity and the advantage of this method are verified by an instance.

引用

页码：2531 / +

页数：2

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