This paper deals with the multivariate vector analysis of quality data by geometric transformation, rotating coordinates and computation of the principal component of initial variables.
本文讨论多元质量数据的矢量分析方法，几何变换，坐标旋转和初始变量的主分量计算不同变量的主分量值。
The principal component analysis in multivariate statistic analysis is a method of compressing the dimension of vector data by extracting principal typical components from sample data set.
多元统计分析的主成份分析方法是对多维矢量数据提取主要特征分量，以此达到压缩矢量维数的目的。
On the basis of analysis of several methods for modeling, a soft sensor based on kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed.
在具体分析了多种建模方法的基础上，提出了核主元分析结合最小二乘支持向量机软测量建模方法。
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