Discriminant Analysis with Tensor Representation

In this paper, we present a novel approach to solving the supervised dimensionality reduction problem by encoding an image object as a general tensor of 2nd or higher order. First, we propose a Discriminant Tensor Criterion (DTC), whereby multiple interrelated lower-dimensional discriminative subspaces are derived for feature selection. Then, a novel approach called k-mode Cluster-based Discriminant Analysis is presented to iteratively learn these subspaces by unfolding the tensor along different tensor dimensions. We call this algorithm Discriminant Analysis with Tensor Representation (DATER), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes; 2) for classification problems involving higher-order tensors, the DATER algorithm can avoid the curse of dimensionality dilemma and overcome the small sample size problem; and 3) the computational cost in the learning stage is reduced to a large extent owing to the red...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Shuicheng Yan, Dong Xu, Qiang Yang, Lei Zhang, Xiaoou Tang, HongJiang Zhang
• Keywords: Algorithm Discriminant Analysis, Computer Vision, CVPR 2005, Dater Algorithm, Discriminant Tensor Criterion, Lower-dimensional Discriminative Subspaces, Multiple Interrelated Subspaces