说明：  CA(Principal Component Analysis)，即主成分分析方法，是一种使用最广泛的数据降维算法。PCA的主要思想是将n维特征映射到k维上，这k维是全新的正交特征也被称为主成分，是在原有n维特征的基础上重新构造出来的k维特征。
(PCA(Principal Component Analysis) is the most widely used data dimensionality reduction algorithm. The main idea of PCA is to map the n-dimensional feature to the k-dimensional feature, which is a new orthogonal feature also known as the principal component and a k-dimensional feature reconstructed on the basis of the original n-dimensional feature.)