Yu, H. and Bennamoun, M., 2006. 1D-PCA, 2D-PCA to nD-PCA. In: ICPR 2006: Proceedings of 18th International Conference on Pattern Recognition, 2006. IEEE Press.
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In this paper, we first briefly reintroduce the 1D and 2D forms of the classical principal component analysis (PCA). Then, the PCA technique is further developed and extended to an arbitrary n-dimensional space. Analogous to 1D- and 2D-PCA, the new nD-PCA is applied directly to n-order tensors (n ges 3) rather than 1-order tensors (1D vectors) and 2-order tensors (2D matrices). In order to avoid the difficulties faced by tensors computations (such as the multiplication, general transpose and Hermitian symmetry of tensors), our proposed nD-PCA algorithm has to exploit a newly proposed higher-order singular value decomposition (HO-SVD). To evaluate the validity and performance of nD-PCA, a series of experiments are performed on the FRGC 3D scan facial database
|Item Type:||Book Section|
|Additional Information:||18 September 2006 , Hongkong, China.|
|Subjects:||Generalities > Computer Science and Informatics|
|Group:||Media School > National Centre for Computer Animation|
|Deposited By:||Mr Hongchuan Yu|
|Deposited On:||21 May 2010 12:20|
|Last Modified:||07 Mar 2013 15:29|
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