Adaptive Regularization Based Kernel Two Dimensional Discriminant Analysis
JIANG Wei1, ZHANG Jing1, YANG Bing-Ru2
1.School of Mathematics, Liaoning Normal University, Dalian 1160292. 2.School of Computer and Communication Engineering, University of Science and Technology Beijing,Beijing 100083
Abstract In traditional semi-supervised dimension reduction techniques, the manifold regularization term is defined in the original feature space. However, its construction is useless in the subsequent classification. In this paper, adaptive regularization based kernel two dimensional discriminant analysis (ARKTDDA) is presented. Firstly, each image matrix is transformed as the product of two orthogonal matrices and a diagonal matrix by using the singular value decomposition method. The column vectors of two orthogonal matrices are transformed into high dimensional space by two kernel functions. Then, the adaptive regularization is defined in the low dimensional feature space, and it is integrated with two dimensional matrix nonlinear method into one single objective function. By altering iterative optimization, the discriminative information is extracted in two kernel subspaces. Finally, experimental results on two face datasets demonstrate that the proposed algorithm obtains considerable improvement in classification accuracy.
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2014, Vol. 27 
