We consider the problem of learning a structured and discriminative dictionary based on sparse representation for classification task. The structure comprises class-shared and class-specific partitions which allows the separation of common and class-specific information in the data for classification. The resulting optimization problem was a max margin formulation that exploits the hinge loss function property. Comparative evaluation of the proposed classifier against four recent alternatives in a gender classification task indicates a 3-percenatge point improvement.
Zhang, Y., Ogunbona, P. O., Li, W. & Wallace, G. G. (2016). Learning structured dictionary based on inter-class similarity and representative margins. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 2399-2403). United States of America: The Institute of Electrical and Electronics Engineers, Inc.