机器学习中的PCA降维方法研究及其应用  

A Research on PCA Dimension Reduction with Its Application in Machine Learning

作　　者：孙平安[1,2] 王备战[3] SUN Ping’an;WANG Beizhan(Laboratory Management Center,Wuyi University,Wuyishan Fujian 354300,China;The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions,Wuyi University,Wuyishan Fujian 354300,China;School of Software,Xiamen University,Xiamen Fujian 361005,China)

机构地区：[1]武夷学院实验室管理中心,福建武夷山354300 [2]武夷学院认知计算与智能信息处理福建省高校重点实验室,福建武夷山354300 [3]厦门大学软件学院,福建厦门361005

出　　处：《湖南工业大学学报》2019年第1期73-78,共6页Journal of Hunan University of Technology

基　　金：福建省教育厅科技基金资助项目(JAT160524);武夷学院转型发展教育教学改革与研究基金资助项目(xj2014018)

摘　　要：机器学习中涉及大量以图像处理为代表的高维数据,PCA作为有效的数据降维方法常被用于数据预处理阶段。讨论了PCA的K-L数据转换原理、具体降维处理过程、高维样本协方差矩阵的求解技巧、维数选择方法,并在ORL人脸图样库上给出了基于PCA的人脸识别准确度分析。Machine learning involves a large number of high-dimensional data represented by image processing.PCA,as an effective data dimension reduction method,is often applied for data preprocessing.A tentative inquiry has been made into the principle of K-L data conversion,the specific dimension reduction processing,the co-variance matrix of the high dimensional sample and the method of dimension selection,followed by an accuracy analysis of face recognition based on PCA from ORL face pattern database.

关 键 词：PCA K-L变换 线性降维 人脸识别 机器学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]