TITLE:
An Overview of Principal Component Analysis
AUTHORS:
Sasan Karamizadeh, Shahidan M. Abdullah, Azizah A. Manaf, Mazdak Zamani, Alireza Hooman
KEYWORDS:
Biometric; PCA; Eigenvector; Covariance; Standard Deviation
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.4 No.3B,
October
17,
2013
ABSTRACT:
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.