JSIP  Vol.4 No.3 B , August 2013
An Overview of Principal Component Analysis
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.
Cite this paper: S. Karamizadeh, S. Abdullah, A. Manaf, M. Zamani and A. Hooman, "An Overview of Principal Component Analysis," Journal of Signal and Information Processing, Vol. 4 No. 3, 2013, pp. 173-175. doi: 10.4236/jsip.2013.43B031.

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