文献类型：期刊文献

英文题名：Fast Second-Order Orthogonal Tensor Subspace Analysis for Face Recognition

作者：Zhou, Yujian[1];Bao, Liang[2];Lin, Yiqin[1]

机构：[1]Hunan Univ Sci & Engn, Inst Computat Math, Dept Math & Computat Sci, Yongzhou 425100, Peoples R China;[2]E China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China

年份：2014

外文期刊名：JOURNAL OF APPLIED MATHEMATICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000330507400001)】；

基金：Yiqin Lin is supported by the National Natural Science Foundation of China under Grant 10801048, the Natural Science Foundation of Hunan Province under Grant 11JJ4009, the Scientific Research Foundation of Education Bureau of Hunan Province for Outstanding Young Scholars in University under Grant 10B038, the Science and Technology Planning Project of Hunan Province under Grant 2010JT4042, and the Chinese Postdoctoral Science Foundation under Grant 2012M511386. Liang Bao is supported by the National Natural Science Foundation of China under Grants 10926150 and 11101149 and the Fundamental Research Funds for the Central Universities.

语种：英文

摘要：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.