Class mean‐weighted discriminative collaborative representation for classification

International Journal of Intelligent SystemsVolume 36, Issue 7 p. 3144-3173 RESEARCH ARTICLE Class mean-weighted discriminative collaborative representation for classification Jianping Gou, Corresponding Author Jianping Gou [email protected] orcid.org/0000-0002-8438-7286 School of Computer Science and Communication Engineering, Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace, Jiangsu University, Zhenjiang, China Correspondence Jianping Gou, School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China. Email: [email protected]Search for more papers by this authorJun Song, Jun Song orcid.org/0000-0002-9191-9375 School of Computer Science and Communication Engineering, Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace, Jiangsu University, Zhenjiang, ChinaSearch for more papers by this authorLan Du, Lan Du orcid.org/0000-0002-9925-0223 Faculty of Information Technology, Monash University, Melbourne, AustraliaSearch for more papers by this authorShaoning Zeng, Shaoning Zeng orcid.org/0000-0002-4384-8787 School of Information Science and Technology, Huizhou University, Huizhou, ChinaSearch for more papers by this authorYongzhao Zhan, Yongzhao Zhan orcid.org/0000-0001-7475-2895 School of Computer Science and Communication Engineering, Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace, Jiangsu University, Zhenjiang, ChinaSearch for more papers by this authorZhang Yi, Zhang Yi orcid.org/0000-0001-5514-0359 School of Computer Science, Sichuan University, Chengdu, ChinaSearch for more papers by this author Jianping Gou, Corresponding Author Jianping Gou [email protected] orcid.org/0000-0002-8438-7286 School of Computer Science and Communication Engineering, Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace, Jiangsu University, Zhenjiang, China Correspondence Jianping Gou, School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China. Email: [email protected]Search for more papers by this authorJun Song, Jun Song orcid.org/0000-0002-9191-9375 School of Computer Science and Communication Engineering, Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace, Jiangsu University, Zhenjiang, ChinaSearch for more papers by this authorLan Du, Lan Du orcid.org/0000-0002-9925-0223 Faculty of Information Technology, Monash University, Melbourne, AustraliaSearch for more papers by this authorShaoning Zeng, Shaoning Zeng orcid.org/0000-0002-4384-8787 School of Information Science and Technology, Huizhou University, Huizhou, ChinaSearch for more papers by this authorYongzhao Zhan, Yongzhao Zhan orcid.org/0000-0001-7475-2895 School of Computer Science and Communication Engineering, Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace, Jiangsu University, Zhenjiang, ChinaSearch for more papers by this authorZhang Yi, Zhang Yi orcid.org/0000-0001-5514-0359 School of Computer Science, Sichuan University, Chengdu, ChinaSearch for more papers by this author First published: 11 March 2021 https://doi.org/10.1002/int.22411Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Representation-based classification (RBC) has been attracting a great deal of attention in pattern recognition. As a typical extension to RBC, collaborative representation-based classification (CRC) has demonstrated its superior performance in various image classification tasks. Ideally, we expect that the learned class-specific representations for a testing sample are discriminative, and the representation computed for the true class dominates the final representation of the testing sample. Most existing CRC-based methods can learn pattern discrimination, but cannot differentiate the contribution of class-specific representations to the classification of each testing sample. It is challenging for a representation-based classifier to retain both properties. To address this challenge and further improve CRC's classification performance, we propose a novel CRC-based method, class mean-weighted discriminative collaborative representation-based classifier (CMW-DCRC). Its objective function penalises the standard l 2 -norm residuals with two discriminative regularisation terms. A decorrelating term makes the class-specific representations more discriminative, and a newly designed class mean-weighted term that promotes the training samples from individual classes to competitively reconstruct the testing sample while boosting the contribution of the true class. To further enhance the robustness of CRC, we extend CMW-DCRC by replacing the l2-norm coding residual with a l1-norm coding residual, and solve the optimisation problem with an iteratively reweighted least square algorithm. Extensive experimental results on nine image data sets have shown that our methods outperform the state-of-the-art RBC-based methods. Citing Literature Volume36, Issue7July 2021Pages 3144-3173 RelatedInformation