THE REVERSE LOGISTICS EVALUATION BASED ON KPCA-LINMAP MODEL
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.
Reverse Logistics Evaluation Principal Components Analysis Kernel Function the Coupling Model of LINMAP
CAI-QING ZHANG YAN-CHAO LU
Dept.of Economic Management, North China Electric Power University, Baoding, 071003, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2531-2535
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）