1D-PCA, 2D-PCA to nD-PCA

Authors:
Hongchuan Yu;Mohammed Bennamoun
Affiliations:
University of Western Australia, Perth, WA6009, Australia;University of Western Australia, Perth, WA6009, Australia
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Year:
2006

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Abstract

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 . 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.