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在梳理和分析主成分分析用于社会科学评价中存在的问题的基础上,将主成分评价拓展到灰色系统领域,构建了灰色主成分评价模型,以灰色相对关联度矩阵代替传统的协方差矩阵或者相关矩阵来进行主成分分析和评价.此外,根据目前主成分评价中权重引起的重复加权问题做一定的讨论,以因子负荷量作为主成分权重合成的重要依据,解决了重要性权对数据的影响问题.最后,构建了两个案例证明所提思想的合理性和有效性.
On the basis of combing and analyzing the problems existing in the evaluation of social sciences by principal component analysis, this paper extends the evaluation of principal components to the field of gray system, builds a gray principal component evaluation model, replaces the traditional covariance matrix Or correlation matrix for principal component analysis and evaluation.Moreover, based on the present weighting of the principal components in the evaluation of the repeated weighting issues to be discussed, factor load as an important basis for the synthesis of the weight of the principal components, to solve the importance of weight data Finally, we construct two cases to prove the rationality and effectiveness of the proposed ideas.