Weighted Non-negative Sparse Low-rank Representation Classification

摘要：

　　In the calculation of rank minimization,the non-negative sparse low-rank representation classification(NSLRRC)regularizes nuclear norms each singular value equally,but this limits its flexibility and ability to solve many practical problems,where the singular values with clear physical meanings ought to be treated differently.In this paper,a weighted non-negative sparse low-rank representation classification method(WNSLRRC)is proposed for robust face recognition.Our method adaptively assigns weights,which provides additional discriminating ability to the original non-negative sparse low-rank models for improved performance,on different singular values.Our method is able to assess the test sample and correct classification based on class-specific reconstruction residuals.Experimental results on public face databases testify the robustness and effectiveness of our method in face recognition.Those also show that our method outperforms other state-of-the-art methods.

关键词： Robust Face recognition Sparse Low-rank Classification

作者: Jingshan Li Caikou Chen Xielian Hou Tianchen Dai Rong Wang

作者单位: College of Information Engineering Yangzhou University Yangzhou,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 2153-2157

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）