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A new discriminative collaborative representation-based classification method via l2 regularizations

  • Jianping GouEmail author
  • Bing Hou
  • Yunhao Yuan
  • Weihua Ou
  • Shaoning Zeng
Original Article

Abstract

Collaborative representation-based classification (CRC) is one of the famous representation-based classification methods in pattern recognition. However, a testing sample in most of the CRC variants is collaboratively reconstructed by a linear combination of all the training samples from all the classes, the training samples from the class that the testing sample belongs to have no advantage in discriminatively and competitively representing and classifying the testing sample. Moreover, the incorrect classification can easily come into being when the training samples from the different classes are very similar. To address the issues, we propose a novel discriminative collaborative representation-based classification (DCRC) method via \(l_2\) regularizations to enhance the power of pattern discrimination. In the proposed model, we consider not only the discriminative decorrelations among all the classes, but also the similarities between the reconstructed representation of all the classes and the class-specific reconstructed representations in the \(l_2\) regularizations. The experiments on several public face databases have demonstrated that the proposed DCRC effectively and robustly outperforms the state-of-the-art representation-based classification methods.

Keywords

Collaborative representation Representation-based classification Pattern recognition 

Notes

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61502208, 61762021 and 61402122), Natural Science Foundation of Jiangsu Province of China (Grant No. BK20150522), International Postdoctoral Exchange Fellowship Program of China Postdoctoral Council (No. 20180051), Research Foundation for Talented Scholars of JiangSu University (Grant No. 14JDG037), China Postdoctoral Science Foundation (Grant No. 2015M570411), Open Foundation of Artificial Intelligence Key Laboratory of Sichuan Province (Grant No. 2017RYJ04), and Natural Science Foundation of Guizhou Province (Nos. [2017]1130 and [2017]5726-32). The authors would like to thank Jia Ke for some helpful suggestions and discussions for the revisions of this article. Furthermore, they thank the editors and reviewers for valuable comments to improve our article.

Compliance with ethical standards

Conflict of interest

All the authors declare that there is no conflict of interests regarding the publication of this article.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial CyberspaceJiangsu UniversityZhenjiangPeople’s Republic of China
  2. 2.Department of Computer Science and TechnologyYangzhou UniversityYangzhouPeople’s Republic of China
  3. 3.School of Big Data and Computer ScienceGuizhou Normal UniversityGuiyangPeople’s Republic of China
  4. 4.School of Information Science and TechnologyHuizhou UniversityHuizhouPeople’s Republic of China

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