Principal component analysis

被引：20
作者：
Wallen, Hayley
机构：
[1] Department of Economics and Business, Universitat Pompeu Fabra and Barcelona School of Management, Barcelona
[2] Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam
[3] Departments of Statistics and Biomedical Science, Stanford University, Stanford, CA
[4] Department of Political Sciences, University of Naples Federico II, Naples
[5] Department of Primary Education, Democritus University of Thrace, Alexandroupolis
[6] Department of Statistics, Stanford University, Stanford, CA
来源：
NATURE REVIEWS METHODS PRIMERS | 2022年 / 2卷 / 01期
基金：
美国国家卫生研究院; 美国国家科学基金会;
关键词：
D O I：
10.1038/s43586-022-00192-w
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Principal component analysis (PCA) reduces large, multidimensional datasets into fewer variables, called principal components (PCs). Each PC is a linear combination of the original variables, which maximally explains the variance of all original variables. This approach enables complex datasets to be interpreted.
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页数：1
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