An Overview of Principal Component Analysis

• 作者: Alireza Hooman Azizah A. Manaf Mazdak Zamani Sasan Karamizadeh Shahidan M. Abdullah
• 来源期刊: 信号与信息处理(英文)
• 学科: 数学
• 关键词: BIOMETRIC PCA EIGENVECTOR COVARIANCE STANDARD Deviation
• 年，卷（期）: xhyxxclyw_2013,（3）
• 页码范围: 173-175
• 页数: 3页
• 分类号: O1

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.