Principle component analysis based computing in image recognition

Charles Z. Liu; Manolya Kavakli-Thorne

Principle component analysis based computing in image recognition

Charles Z. Liu^*, Manolya Kavakli-Thorne

^*Corresponding author for this work

Research output: Chapter in Book/Published conference output › Chapter

Abstract

This chapter mainly focuses on Principle Component Analysis (PCA) based method for image computing. The underlying mechanism of PCA and several significant factors involved in subspace training are theoretically discussed including principle components' energy, residuals assessment, and decomposition computation. The typical extensions, including Probabilistic PCA (PPCA), Kernel PCA (KPCA), multidimensional PCA and Robust PCA (RPCA), have been critically analysed in this chapter.

Original language	English
Title of host publication	Image Recognition
Subtitle of host publication	Progress, Trends and Challenges
Publisher	Nova Science Publishers Inc
Pages	31-66
Number of pages	36
ISBN (Electronic)	9781536172591
ISBN (Print)	9781536172584
Publication status	Published - 12 May 2020

Bibliographical note

Publisher Copyright:
© 2020 by Nova Science Publishers, Inc. All rights reserved.

Keywords

Eigenspace
Extended PCA
Image recognition
Karhunen-Loeve transform
Principle component analysis
Subspace training

Cite this

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title = "Principle component analysis based computing in image recognition",

abstract = "This chapter mainly focuses on Principle Component Analysis (PCA) based method for image computing. The underlying mechanism of PCA and several significant factors involved in subspace training are theoretically discussed including principle components' energy, residuals assessment, and decomposition computation. The typical extensions, including Probabilistic PCA (PPCA), Kernel PCA (KPCA), multidimensional PCA and Robust PCA (RPCA), have been critically analysed in this chapter.",

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AB - This chapter mainly focuses on Principle Component Analysis (PCA) based method for image computing. The underlying mechanism of PCA and several significant factors involved in subspace training are theoretically discussed including principle components' energy, residuals assessment, and decomposition computation. The typical extensions, including Probabilistic PCA (PPCA), Kernel PCA (KPCA), multidimensional PCA and Robust PCA (RPCA), have been critically analysed in this chapter.

KW - Eigenspace

KW - Extended PCA

KW - Image recognition

KW - Karhunen-Loeve transform

KW - Principle component analysis

KW - Subspace training

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M3 - Chapter

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BT - Image Recognition

PB - Nova Science Publishers Inc

ER -

Principle component analysis based computing in image recognition

Abstract

Bibliographical note

Keywords

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