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Locality-constrained weighted collaborative-competitive representation for classification

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Abstract

How to represent and classify a testing sample for the representation-based classification (RBC) plays an important role in the filed of pattern recognition. As a typical kind of the representation-based classification with promising performance, collaborative representation-based classification (CRC) adopts all the training samples to collaboratively represent and then classify each testing sample with the reconstructive residuals among all the classes. However, most of the CRC methods fail to make full use of the localities and discrimination information of data in collaborative representation. To address this issue to further improve the classification performance, we design a novel supervised CRC method entitled locality-constrained weighted collaborative-competitive representation-based classification (LWCCRC). In the proposed method, the localities of data are taken into account by using the positive and negative nearest samples of each testing sample with their corresponding weighted constraints. Such devised locality-constrained weighted term can model the similarity and natural discrimination information contained in the neighborhood region for each testing sample to obtain the favorable representation. Moreover, a competitive constraint is introduced to enhance pattern discrimination among the categorical collaborative representations. To explore the effectiveness of our proposed LWCCRC, the extensive experiments are carried out on three different types of data sets. The experimental results demonstrate that the proposed LWCCRC significantly outperforms the recent state-of-the-art CRC methods.

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Notes

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Acknowledgements

This work was supported in part in by National Natural Science Foundation of China (Grant Nos. 61976107, 61962010, 61762021 and 61502208), Postgraduate Research and Practice Innovation Program of Jiangsu Province (Grant No. SJCX20_1415), Qing Lan Project of Colleges and Universities of Jiangsu Province in 2020, and Excellent Young Scientific and Technological Talent of Guizhou Provincical Science and Technology Foundation ([2019]-5670).

Author information

Authors and Affiliations

  1. School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace, Jiangsu University, Zhenjiang, Jiangsu, 212013, People’s Republic of China

    Jianping Gou, Xiangshuo Xiong & Hongwei Wu

  2. The faculty of information technology, Monash University, Clayton, 3800, Australia

    Lan Du

  3. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313000, People’s Republic of China

    Shaoning Zeng

  4. School of Computer Science and Technology, Yangzhou University, Yangzhou, Jiangsu, 225127, People’s Republic of China

    Yunhao Yuan

  5. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, Guizhou, 550025, People’s Republic of China

    Weihua Ou

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  1. Jianping Gou
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  3. Hongwei Wu
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  4. Lan Du
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Correspondence to Jianping Gou or Weihua Ou.

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Gou, J., Xiong, X., Wu, H. et al. Locality-constrained weighted collaborative-competitive representation for classification. Int. J. Mach. Learn. & Cyber. 14, 363–376 (2023). https://doi.org/10.1007/s13042-021-01461-y

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