说明：  主成分分析的主要目的是希望用较少的变量去解释原来资料中的大部分变异，将我们手中许多相关性很高的变量转化成彼此相互独立或不相关的变量。通常是选出比原始变量个数少，能解释大部分资料中的变异的几个新变量，即所谓主成分，并用以解释资料的综合性指标。由此可见，主成分分析实际上是一种降维方法。
(The main purpose of PCA is to use fewer variables to explain most of the variation of the original data will be in our hands a number of highly relevant independent variables into each other or irrelevant variables. Is usually higher than the original number of variables selected less able to explain most of the information in the variation of several new variables, called principal components, and to explain the comprehensive index of information. Thus, principal component analysis is a kind of dimension reduction methods.)