文章基本信息

标题：An Overview of Principal Component Analysis
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作者：Sasan Karamizadeh ; Shahidan M. Abdullah ; Azizah A. Manaf 等
期刊名称：Journal of Signal and Information Processing
印刷版ISSN：2159-4465
电子版ISSN：2159-4481
出版年度：2013
卷号：04
期号：03
页码：173-175
DOI：10.4236/jsip.2013.43B031
语种：English
出版社：Scientific Research Publishing
摘要：The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.
关键词：Biometric; PCA; Eigenvector; Covariance; Standard Deviation