Open Access
2014 Fast Second-Order Orthogonal Tensor Subspace Analysis for Face Recognition
Yujian Zhou, Liang Bao, Yiqin Lin
J. Appl. Math. 2014: 1-11 (2014). DOI: 10.1155/2014/871565


Tensor subspace analysis (TSA) and discriminant TSA (DTSA) are two effective two-sided projection methods for dimensionality reduction and feature extraction of face image matrices. However, they have two serious drawbacks. Firstly, TSA and DTSA iteratively compute the left and right projection matrices. At each iteration, two generalized eigenvalue problems are required to solve, which makes them inapplicable for high dimensional image data. Secondly, the metric structure of the facial image space cannot be preserved since the left and right projection matrices are not usually orthonormal. In this paper, we propose the orthogonal TSA (OTSA) and orthogonal DTSA (ODTSA). In contrast to TSA and DTSA, two trace ratio optimization problems are required to be solved at each iteration. Thus, OTSA and ODTSA have much less computational cost than their nonorthogonal counterparts since the trace ratio optimization problem can be solved by the inexpensive Newton-Lanczos method. Experimental results show that the proposed methods achieve much higher recognition accuracy and have much lower training cost.


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Yujian Zhou. Liang Bao. Yiqin Lin. "Fast Second-Order Orthogonal Tensor Subspace Analysis for Face Recognition." J. Appl. Math. 2014 1 - 11, 2014.


Published: 2014
First available in Project Euclid: 26 March 2014

zbMATH: 1405.68313
MathSciNet: MR3166793
Digital Object Identifier: 10.1155/2014/871565

Rights: Copyright © 2014 Hindawi

Vol.2014 • 2014
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