Learning structured dictionary based on inter-class similarity and representative margins

Authors

Yuyao Zhang, University of WollongongFollow
Philip O. Ogunbona, University of WollongongFollow
Wanqing Li, University of WollongongFollow
Gordon G. Wallace, University of WollongongFollow

RIS ID

108003

Publication Details

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.

Abstract

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.