Department of Maths of University of Shaoxing, Shaoxing, China

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Abstract—A new semi-supervised dimensionality reduction method called Semi-supervised Tensor Graph-optimized Linear Discriminant Analysis (STGLDA) is proposed for two-dimensional face recognition. Unlike recent proposed Tensor Locally Linear Discriminative Analysis (TLLDA), STGLDA offers some advantages over TLLDA. 1) STGLDA is originated from Graph-based Fisher Analysis (GbFA) while TLLDA is originated from the Local Fisher Discriminant Analysis (LFDA). In contrast to LFDA, GbFA encodes the richer discriminating information and there is no assumption that data obeys Gaussian distribution. 2) With the linear weighted way of fusing Principal Component Analysis (PCA) and GbFA, STGLDA preserves global scatter structure information and Graph-based discriminant information capturing local structure feature. TLLDA only pays attentions to preserve local structure information and discriminant information while ignores preserving global structure information. Experimental results on real face databases demonstrate that STGLDA is highly competitive with TLLDA and other tensor dimensionality reduction algorithm.

Index Terms—Face recognition, semi-supervised dimensionality reduction, linear discriminant analysis, tensor representation, graph optimization, information infusion, trade-off parameter.

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Cite: Shuhua Xu, "Semi-supervised Tensor Graph-optimized Linear Discriminant Analysis for Two-dimensional Face Recognition," Journal of Software vol. 8, no. 8, pp. 2047-2051, 2013.