Discriminative Dictionary Learning for Tensor Data
Selin Aviyente, Department of Electrical and Computer Engineering, Michigan State University, USA
Dictionary learning methods aim to learn atoms that can best represent a signal class. In recent years, these methods have been extended to learn discriminative dictionaries such that the learned atoms are specific to each class enabling better classification accuracy. With the increase of high dimensional and multi-aspect data, there is a growing need to extend dictionary learning algorithms to tensor type data. In this talk, we propose an efficient, separable and orthogonal dictionary structure for learning class-specific dictionaries for tensor objects. The proposed cost function tries to minimize the representation error as well as within-class scatter while putting a sparsity constraint on the learned representation. The algorithm is applied to different tensor object classification tasks with extensive evaluations of the effect of sparsity, discriminability and reconstruction error on classification accuracy.