主成分分析方法综述

A Review of Principal Component Analysis

主成分分析是一种非常有效的数据分析处理的技术,具有非常广泛的应用前景。本文首先概述了主成分分析方法,然后介绍了PCA的定义、模型、算法及选取主成分个数的标准,对PCA技术的优势和缺陷分别进行了剖析和总结,对PCA在评价排序、特征提取、模式识别、图像处理、图像分类和图像压缩等领域的实际应用进行了讨论,对主成分分析方法的发展趋势和应用前景做了展望。

PCA（Principal Component Analysis）is an effective data analysis technique with a bright future of extensive application.The paper summarizes PCA in the first place,and then introduces its definition,data model,algorithm and the standards to determine the number of selected principal components.Moreover,the paper analyzes and summarizes the advantages and disadvantages of the PCA technique,and discusses its practical application in different fields,like evaluation and sorting,feature extraction,patter recognition,image processing,image classification and image compression.Finally,the paper makes expectation about the development trend and application prospect.