摘要
核主成分分析(KPCA)方法是通过非线性变换,将指标原变量空间映射到高维特征空间,并在高维特征空间中进行主成分分析的方法。为此,将该方法应用于农业机械性能综合评价的研究中,不仅实现了数据降维,而且还有效地处理了各指标的非线性影响,与其它评价方法相比较,评价结果更客观且指导性较强,取得了良好的效果。
The kernel principal component analysis is a new kind of comprehensive evaluation model. It can effectively compute principal component in multidimensional feature spaces which be transformed from the original variable spaces by using kernel function. It was used to evaluate the performance of farm machinery. The model not only realizes the trans- formation from original variable spaces to multidimensional, and to of one dimension, but also reflects the nonlinear rela- tionship of evaluative factors effectively, and the results are more objective and more instructional compared with others methods.
出处
《农机化研究》
北大核心
2009年第9期187-189,共3页
Journal of Agricultural Mechanization Research
基金
黑龙江科技学院引进人才启动基金项目(2006-2007)
国家“十一五”科技支撑资助项目(2006BAD11A05-6)
关键词
核主成分分析
综合评价
农业机械性能
kernel principal component analysis
comprehensive evaluation
farm machinery performance
