Tensor Linear Discriminant Analysis
David D. Zhang, Fengxi Song, Yong Dong Xu, Zhizhen Liang · Advances in information and communication technology education series/Advances in information and communication technology education (AICTE) book series · 2009
Linear discriminant analysis is a very effective and important method for feature extraction. In general, image matrices are often transformed into vectors prior to feature extraction, which results in the curse of dimensionality when the dimensions of matrices are huge. In this chapter, classical LDA and its several variants are introduced. In some sense, the variants of LDA can avoid the singularity problem and achieve computational efficiency. Experimental results on biometric data show the usefulness of LDA and its variants in some cases.