【作者】 林健； 朱帮助；

【Author】 Lin Jian,Zhu Bangzhu Institute of System Science and Technology, Wuyi University, Jingmen 529020,China Beijing University of Aeronautics and Astronautics, Beijing 100083, China

【机构】 五邑大学系统科学与技术研究所；

【摘要】 为解决主成分分析(PCA)在多指标综合评价中非线性分析上的不足,提出了综合评价的核主成分分析(KPCA) 方法。利用核函数将原空间映射到高维特征空间,在高维空间进行线性主成分分析;通过对核参数的适当选取,可使得最大特征值的贡献率达到或接近85％,避免了多个主成分的不同组合而导致评价结果不一致。通过对某市经济社会发展综合评价的实证表明,KPCA综合评价方法具有较高的客观性。更多还原

【Abstract】 To solve the drawbacks of principal composition analysis (PCA) used to analyze nonlinear problem in comprehensive evaluation with multiple indicators, kernel principal composition analysis (KPCA) is introduced. By using the kernel functions, one can efficiently calculate principal compositions in high dimensional feature spaces, related in input space by some nonlinear map. By choosing appropriate parameters, the maximum eigenvalue contributes above or nearly 85% , avoiding the different array as a result of many principal compositions. An example is presented to illustrate that KPCA has a high objectivity.更多还原