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N-Dimensional Principal Component Analysis.

Yu, H., 2010. N-Dimensional Principal Component Analysis. In: FCV 2010: Proceedings of 16th Korea-Japan Joint workshop on Frontiers of Computer vision. IEEE Press.

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In this paper, we first briefly introduce the multidimensional Principal Component Analysis (PCA) techniques, and then amend our previous N-dimensional PCA (ND-PCA) scheme by introducing multidirectional decomposition into ND-PCA implementation. For the case of high dimensionality, PCA technique is usually extended to an arbitrary n-dimensional space by the Higher-Order Singular Value Decomposition (HO-SVD) technique. Due to the size of tensor, HO-SVD implementation usually leads to a huge matrix along some direction of tensor, which is always beyond the capacity of an ordinary PC. The novelty of this paper is to amend our previous ND-PCA scheme to deal with this challenge and further prove that the revised ND-PCA scheme can provide a near optimal linear solution under the given error bound. To evaluate the numerical property of the revised ND-PCA scheme, experiments are performed on a set of 3D volume datasets.

Item Type:Book Section
Additional Information:4-6 February 2010, Hiroshima, Japan.
Group:Faculty of Media & Communication
ID Code:14734
Deposited By: Mr Hongchuan Yu
Deposited On:21 May 2010 10:56
Last Modified:14 Mar 2022 13:32


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