主成分分析（Principal Copmponent　Analysis，简称PCA）是一种常用的机遇变量协方差矩阵对信息进行处理、压缩和提取的有效方法。主成分分析，这种方法可以有效的找出数据中最“主要”的元素和结构，去除噪音和冗余，将原有的复杂数据降维，能够发掘出隐藏在复杂数据背后的简单结构。-PCA (Principal Copmponent Analysis, abbreviated PCA) is a commonly used covariance matrix Opportunity information processing, compressing and extracting an effective manner. Principal component analysis, this method can effectively identify the data most " primary"　elements and structures, removing noise and redundancy, the original complex data dimensionality reduction, to discover hidden behind the simple and complex data structures.