IEEE Systems, Man and Cybernetics Magazine - January 2021 - 25

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Some Multilinear
Variants of
Principal
Component
Analysis
Examples in Grayscale Image Recognition
and Reconstruction
by Richard A. Nelson and Rodney G. Roberts

P

rincipal component analysis (PCA) has
long been used in computer vision applications such as face recognition. Here, we
present an overview of some variants of
PCA, including 2D PCA (2DPCA), bidirectional 2DPCA (B2DPCA), and coupled subspace analysis (CSA). Unlike conventiona l PCA, the va r ia nts
2DPCA, B2DPCA, and CSA preserve the original image
Digital Object Identifier 10.1109/MSMC.2020.3012304
Date of current version: 14 January 2021

2333-942X/21©2021IEEE

structure, often providing better recognition and reconstruction results than those obtained with PCA. This
article considers the background for these techniques
and steps involved in applying these methods, including
typical preprocessing of sample images, algorithm
description, and classification. These variants of PCA
have been successfully used in a number of different
areas such as identification of wood species, biometrics
(not limited to face recognition), medical imaging, and
image compression, to name a few examples; we briefly
mention some of these to provide an idea of the scope of
Ja nu a r y 2021

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE

25


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IEEE Systems, Man and Cybernetics Magazine - January 2021

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