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Tensor Principal Component Analysis
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Author(s): David Zhang (Hong Kong Polytechnic University, Hong Kong), Fengxi Song (New Star Research Institute Of Applied Technology, China), Yong Xu (Harbin Institute of Technology, China)and Zhizhen Liang (Shanghai Jiao Tong University, China)
Copyright: 2009
Pages: 22
Source title:
Advanced Pattern Recognition Technologies with Applications to Biometrics
Source Author(s)/Editor(s): David Zhang (Hong Kong Polytechnic University, Hong Kong ), Fengxi Song (New Star Research Institute Of Applied Technology, China), Yong Xu (Harbin Institute of Technology, China)and Zhizhen Liang (Shanghai Jiao Tong University, China)
DOI: 10.4018/978-1-60566-200-8.ch008
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Abstract
Tensor principal component analysis (PCA) is an effective method for data reconstruction and recognition. In this chapter, some variants of classical PCA are introduced and the properties of tensor PCA are analyzed. Section 8.1 gives the background and development of tensor PCA. Section 8.2 introduces tensor PCA. Section 8.3 discusses some potential applications of tensor PCA in biometrics. Finally, we summarize this chapter in Section 8.4.
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