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 |
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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