标题:An adaptive sparse representation model by block dictionary and swarm intelligence
作者:Li, Fei ;Jiang, Mingyan ;Zhang, Zhenyue
通讯作者:Jiang, Mingyan
作者机构:[Li, Fei ;Jiang, Mingyan ;Zhang, Zhenyue ] School of Information Science and Engineering, Shandong University, Jinan, Shandong; 250100, China
会议名称:2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
会议日期:8 September 2017 through 11 September 2017
来源:2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
出版年:2017
卷:2017-January
页码:200-203
DOI:10.1109/CIAPP.2017.8167207
关键词:Adaptive; Face recognition; Sparse representation; Swarm intelligence
摘要:The pattern recognition in the sparse representation (SR) framework has been very successful. In this model, the test sample can be represented as a sparse linear combination of training samples by solving a norm-regularized least squares problem. However, the value of regularization parameter is always indiscriminating for the whole dictionary. To enhance the group concentration of the coefficients and also to improve the sparsity, we propose a new SR model called adaptive sparse representation classifier(ASRC). In ASRC, a sparse coefficient strengthened item is added in the objective function. The model is solved by the artificial bee colony (ABC) algorithm with variable step to speed up the convergence. Also, a partition strategy for large scale dictionary is adopted to lighten bee's load and removes the irrelevant groups. Through different data sets, we empirically demonstrate the property of the new model and its recognition performance. © 2017 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043449306&doi=10.1109%2fCIAPP.2017.8167207&partnerID=40&md5=0e63d555b4a232efbe2f96d51272c0ea
TOP