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Tensor Independent Component Analysis and Tensor Non-Negative Factorization
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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: 24
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.ch010
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Abstract
In this chapter, we describe two tensor-based subspace analysis approaches (tensor ICA and tensor NMF) that can be used in many fields like face recognition and other biometric recognition. Section 10.1 gives the background and development of the two tensor-based subspace analysis approaches. Section 10.2 introduces tensor independent component analysis. Section 10.3 presents tensor nonnegative factorization. Section 10.4 discusses some potential applications of these two subspace analysis approaches in biometrics. Finally, we summarize this chapter in Section 10.5.
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