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该数据经过PCA处理之后，得到的标准化数据的相关系数矩阵，并使用Jac
该数据经过PCA处理之后，得到的标准化数据的相关系数矩阵，并使用Jacobian矩阵，以获得相关系数矩阵的特征值、统一特征向量和方差贡献率（Table1）。碎石图(Fig.2)中可以看出：当特征值大于1，主成分数目为4, 方差贡献率分别为31.46%、19.06%、18.27%、11.48%。四个主成分的方差累计贡献率达80.27%，各个变量丢失的信息较少，一定程度的满足了缩减计算变量的需求。因此选取15个成分中的主成分个数为4是合理的。
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After the data is processed by PCA, the correlation coefficient matrix of the standardized data is obtained, and the Jacobian matrix is used to obtain the eigenvalues, unified eigenvectors and variance contribution rate of the correlation coefficient matrix (Table1). It can be seen from the gravel map (Fig.2) that when the eigenvalue is greater than 1, the number of principal components is 4, and the variance contribution rates are 31.46%, 19.06%, 18.27%, and 11.48%, respectively. The cumulative contribution rate of the variance of the four principal components reached 80.27%, and the missing information of each variable was less, which satisfies the need to reduce the calculation variables to a certain extent. Therefore, it is reasonable to select 4 principal components among 15 components.
正在翻译中..
After the PCA processing of the data, the correlation coefficient matrix of the standardized data is obtained, and the Jacobian matrix is used to obtain the characteristic value, uniform feature vector, and variance contribution rate (Table1) of the correlation coefficient matrix. As can be seen in the gravel map (Fig.2), when the characteristic value is greater than 1 and the number of main components is 4, the variance contribution rate is 31.46%, 19.06%, 18.27% and 11.48%, respectively. The cumulative contribution rate of variance of the four main components is 80.27%, each variable loses less information, and the need to reduce the calculated variable is satisfied to some extent. Therefore, it is reasonable to select the number of main ingredients in 15 ingredients to be 4.
正在翻译中..
After PCA processing, the correlation coefficient matrix of standardized data is obtained, and Jacobian matrix is used to obtain the eigenvalue, unified eigenvector and variance contribution rate of correlation coefficient matrix (table1). It can be seen from the gravel map (Fig. 2): when the characteristic value is greater than 1, the main component is 4, and the variance contribution rates are 31.46%, 19.06%, 18.27% and 11.48%, respectively. The cumulative contribution rate of variance of the four principal components is 80.27%, and the missing information of each variable is less, which meets the need of reducing the calculation variables to a certain extent. Therefore, it is reasonable to select the number of principal components of 15 components as 4.<br>
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