TY - GEN
T1 - Joint collaborative representation and discriminative projection for pattern classification
AU - Li, Junyu
AU - Yuan, Haoliang
AU - Lai, Loi Lei
AU - Cheung, Yiu Ming
N1 - Funding Information:
This research work was supported in part by the Guangdong University of Technology, Guangzhou, China under Grant from the Financial and Education Department of Guangdong Province 2016[202]: Key Discipline Construction Programme, in part by the Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group (Grant No. 2016KCXTD022), and in part by the Foundation for Distinguished Young Talents in Higher Education of Guangdong (Grant No. 2016KQNCX045).
PY - 2018/12/5
Y1 - 2018/12/5
N2 - Representation-based classifiers have shown the impressive results for pattern classification. In this paper, we propose a joint collaborative representation and discriminative projection model (JCRDP) for subspace learning. We aim to seek a linear projection matrix to effectively reveal or maintain the underlying structure of original data and well fit collaborative representation classifier simultaneously. Unlike previous representation-based subspace learning methods, in which the linear reconstruction and the generalized eigenvalue decomposition are two independent steps, our proposed JCRDP integrates these two tasks into one single optimization step to learn a more discriminative linear projection matrix. To effectively solve JCRDP, we develop an alternative strategy to deal with the optimization problem. Extensive experimental results demonstrate the effectiveness of our proposed method.
AB - Representation-based classifiers have shown the impressive results for pattern classification. In this paper, we propose a joint collaborative representation and discriminative projection model (JCRDP) for subspace learning. We aim to seek a linear projection matrix to effectively reveal or maintain the underlying structure of original data and well fit collaborative representation classifier simultaneously. Unlike previous representation-based subspace learning methods, in which the linear reconstruction and the generalized eigenvalue decomposition are two independent steps, our proposed JCRDP integrates these two tasks into one single optimization step to learn a more discriminative linear projection matrix. To effectively solve JCRDP, we develop an alternative strategy to deal with the optimization problem. Extensive experimental results demonstrate the effectiveness of our proposed method.
KW - Collaborative representation
KW - Pattern classification
KW - Subspace learning
UR - http://www.scopus.com/inward/record.url?scp=85060688675&partnerID=8YFLogxK
U2 - 10.1109/CIS2018.2018.00035
DO - 10.1109/CIS2018.2018.00035
M3 - Conference proceeding
AN - SCOPUS:85060688675
T3 - Proceedings - 14th International Conference on Computational Intelligence and Security, CIS 2018
SP - 125
EP - 129
BT - Proceedings - 14th International Conference on Computational Intelligence and Security, CIS 2018
PB - IEEE
T2 - 14th International Conference on Computational Intelligence and Security, CIS 2018
Y2 - 16 November 2018 through 19 November 2018
ER -