variance contribution meaning in Chinese
方差贡献
Examples
- Regarding the variance contribution rate of each factor as right count weight , this thesis gets the evaluation score and rank
在计算综合得分时是以因子的方差贡献率为权数加权求和，由此得到各地区的综合得分以及排名。 - Spca arithmetic has smaller number of spectral principal components and greater variance contribution than pca by choosing proper kernel functions and parameters
当选取合适的核函数和参数时，谱主成分的个数比主成分的个数要小且累积方差贡献率要大。 - The results of numerical calculations show that : the number of spectral principal component and cumulate variance contribution are different its depending on kernel functions
通过数值例子计算表明：取不同核函数而得到的谱主成分分析，其谱主成分的个数及累积方差贡献率是有差别的。 - Via numeric sample analysis , it is found that evaluation functions are constructed by weighing principal components for pca . however , evaluation functions can be quite different when there are more than three principal components and characteristic vectors other than first one are chosen in different directions . for spca , variance contribution can be greater than 90 % by selecting just one principle component
将谱主成分分析应用于多指标评价系统中，通过数值例子分析：主成分分析是通过对各个主成分加权构造评价函数，当主成分个数不小三个时，从第二个特征向量开始，对方向的不同选取，可导致评价函数的极大差异：而用谱主成分分析，能做到只取一个谱主成分就可使方差贡献率大于90 。 - Therefore , spca gotten via selecting polynomial kernel functions is more accurate than pca in multi - index evaluation system , and has fewer dimensions . comparatively , for spca using gauss kernel function and laplace kernel function , it is required to normalize original data according to the category , and the constructed evaluation functions are better than the ones constructed via using pca . however , if variance contribution of first principal component of pca is more than 85 % , evaluation function curves of spca and pca are similar
在作多指标评价中，选用多项式核函数而得到的谱主成分分析，比主成分分析得到的主成分具有维数低且精度高的优点；而用gauss核函数和laplace核函数的谱主成分分析，需对原数据作同类别数据间的规范化，其构造的评价函数也优于用主成分方法构造的评价函数。