Skip to main content
SpringerLink
Log in

Locality-constrained weighted collaborative-competitive representation for classification

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

  2. http://www.vision.caltech.edu/html-files/archive.html.

  3. http://www.anean.com/research/face.htm.

  4. http://archive.ics.uci.edu/ml.

  5. https://www.cs.ucr.edu/eamonn/time_series_data/.

References

  1. Zhang, L, Yang, M, Feng, X (2011) Sparse representation or collaborative representation: Which helps face recognition?. In: International Conference on Computer Vision (ICCV2011), pp.471-478. https://doi.org/10.1109/ICCV.2011.6126277

  2. Deng W, Hu J, Guo J (2018) Face recognition via collaborative representation: Its discriminant nature and superposed representation. IEEE Trans Pattern Anal Mach Intell 40(10):2513–2521. https://doi.org/10.1109/TPAMI.2017.2757923

    Article  Google Scholar 

  3. Xu J, An W, Zhang L, Zhang D (2019) Sparse, collaborative, or nonnegative representation: which helps pattern classification? Pattern Recognition 88:679–688. https://doi.org/10.1016/j.patcog.2018.12.023

    Article  Google Scholar 

  4. Lan R, Zhou Y, Liu Z, Luo X (2020) Prior knowledge-based probabilistic collaborative representation for visual recognition. IEEE Trans Cybernetics 50(4):1498–1508. https://doi.org/10.1109/TCYB.2018.2880290

    Article  Google Scholar 

  5. Gou J, Wang L, Yi Z, Yuan Y, Ou W, Mao Q (2020) Weighted discriminative collaborative competitive representation for robust image classification. Neural Networks 125:104–120. https://doi.org/10.1016/j.neunet.2020.01.020

    Article  Google Scholar 

  6. Imani Maryam (2020) A collaborative representation-based approximation method for remote sensing image fusion. Int J Remote Sens 41(3):974–995. https://doi.org/10.1080/01431161.2019.1654143

    Article  Google Scholar 

  7. Li ZQ, Sun J, Wu XJ, Yin HF (2020) Multiplication fusion of sparse and collaborative-competitive representation for image classification. Int J Machine Learning and Cybernetics 11(10):2357–2369. https://doi.org/10.1007/s13042-020-01123-5

    Article  Google Scholar 

  8. Liu J, Wu Z, Xiao L, Yan H (2020) Learning multiple parameters for kernel collaborative representation classification. IEEE Transactions on Neural Networks and Learning Systems 31(12):5068–5078. https://doi.org/10.1109/TNNLS.2019.2962878

    Article  Google Scholar 

  9. Liu B, Jing L, Li J, Yu J, Gittens A, Mahoney MW (2019) Group collaborative representation for image set classification. Int J Computer Vision 127(2):181–206. https://doi.org/10.1007/s11263-018-1088-0

    Article  MATH  Google Scholar 

  10. Cai S, Zhang L, Zuo W, Feng X (2016) A probabilistic collaborative representation based approach for pattern classification In: Proceedings of CVPR Las Vegas NV; 2950-2959. https://doi.org/10.1109/CVPR.2016.322

  11. Xu Y, Zhong Z, Yang J et al (2017) A New Discriminative Sparse Representation Method for Robust Face Recognition via l2 Regularization. IEEE Transactions on Neural Networks and Learning Systems 28(10):2233–2242. https://doi.org/10.1109/TNNLS.2016.2580572

    Article  Google Scholar 

  12. Gou J, Hou B, Yuan Y (2020) A new discriminative collaborative representation-based classification method via l2 regularizations. Neural Comput Appl 32:9479–9493. https://doi.org/10.1007/s00521-019-04460-x

    Article  Google Scholar 

  13. Yuan H, Li X, Xu F, Wang Y, Lai LL, Tang YYA (2018) collaborative-competitive representation based classifier model. Neurocomputing 275:627–635. https://doi.org/10.1016/j.neucom.2017.09.022

    Article  Google Scholar 

  14. Gou J, Song J, Du L, Zeng S, Zhan Y, Yi Z (2021) Class mean-weighted discriminative collaborative representation for classification. Int J Intel Sys. https://doi.org/10.1002/int.22411

    Article  Google Scholar 

  15. Chi H, Xia H, Zhang L, Zhang C, Tang X (2020) Competitive and collaborative representation for classification. Pattern Recogn Lett 132:46–55. https://doi.org/10.1016/j.patrec.2018.06.019

    Article  Google Scholar 

  16. Wang J, Yang J, Yu K et al (2010) Locality-constrained Linear Coding for image classification IEEE. Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2010.5540018

    Article  Google Scholar 

  17. Waqas J, Yi Z, Zhang L (2013) Collaborative neighbor representation based classification using l2-minimization approach. Pattern Recognition Letters 34(2):201–208. https://doi.org/10.1016/j.patrec.2012.09.024

    Article  Google Scholar 

  18. Timofte R, Van Gool L (2014) Adaptive and weighted collaborative representations for image classification. Pattern Recogn Lett 43:127–135. https://doi.org/10.1016/j.patrec.2013.08.010

    Article  Google Scholar 

  19. Dong X, Zhang H, Zhu L et al (2018) Weighted locality collaborative representation based on sparse subspace. J Visual Commun Image Represent. https://doi.org/10.1016/j.jvcir.2018.11.030

    Article  Google Scholar 

  20. Peng X, Zhang L, Yi Z et al (2014) Learning Locality-Constrained Collaborative Representation for Face Recognition. Pattern Recognition 47(9):2794–2806. https://doi.org/10.1016/j.patcog.2014.03.013

    Article  Google Scholar 

  21. Zheng C, Wang N (2018) Collaborative representation with k-nearest classes for classification. Pattern Recogn lett 117:30–36. https://doi.org/10.1016/j.patrec.2018.11.005

    Article  Google Scholar 

  22. Jin J, Li Y, Sun L et al (2020) A new local knowledge-based collaborative representation for image recognition. IEEE Access 99:1–1. https://doi.org/10.1109/ACCESS.2020.2989452

    Article  Google Scholar 

  23. Chi H, Xia H, Tang X et al (2018) Supervised neighborhood regularized collaborative representation for face recognition. Multimedia Tools and Applications 77(22):29509–29529. https://doi.org/10.1007/s11042-017-4851-2

    Article  Google Scholar 

  24. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2):210–227. https://doi.org/10.1109/TPAMI.2008.79

    Article  Google Scholar 

  25. Akhtar Naveed, Shafait Faisal, Mian Ajmal (2017) Efficient classification with sparsity augmented collaborative representation. Pattern Recognition 65(Complete):136–145. https://doi.org/10.1016/j.patcog.2016.12.017

    Article  Google Scholar 

  26. Liu Z, Wu XJ, Shu Z (2019) Sparsity augmented discriminative sparse representation for face recognition. Pattern Anal Appl 22(4):1527–1535. https://doi.org/10.1007/s10044-019-00792-5

    Article  Google Scholar 

  27. Zeng S, Gou J, Yang X (2018) Improving sparsity of coefficients for robust sparse and collaborative representation-based image classification. Neural Comput Appl 30(10):2965–2978. https://doi.org/10.1007/s00521-017-2900-4

    Article  Google Scholar 

  28. Zhou J, Zeng S, Zhang B (2020) Two-stage knowledge transfer framework for image classification. Pattern Recognition 107:107529. https://doi.org/10.1016/j.patcog.2020.107529

    Article  Google Scholar 

  29. Xu Y, Zhang D, Yang J, Yang JY (2021) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circ Syst Vid 21(9):1255–1262. https://doi.org/10.1109/TCSVT.2011.2138790

    Article  Google Scholar 

  30. Gou J, Xu Y, Zhang D et al (2018) Two-phase linear reconstruction measure-based classification for face recognition. Information Sciences 433:17–36. https://doi.org/10.1016/j.ins.2017.12.025

    Article  Google Scholar 

  31. Wang G, Shi N (2020) Collaborative representation-based discriminant neighborhood projections for face recognition. Neural Computing and Applications 32(10):5815–5832. https://doi.org/10.1007/s00521-019-04055-6

    Article  Google Scholar 

  32. Zheng Z, Sun H, Zhou Y (2021) Multiple discriminant analysis for collaborative representation-based classification. Pattern Recognition 112:107819. https://doi.org/10.1016/j.patcog.2021.107819

    Article  Google Scholar 

  33. Silva Pedro FB, Marcal Andre RS, da Silva Almeida, Rubim M (2013) Evaluation of features for leaf discimination. International Conferene Image Analysis and Recognition. Springer, Berlin Heidelberg, pp 197–204. https://doi.org/10.1007/978-3-642-39094-4_23

    Book  Google Scholar 

  34. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Machine Learn Res 7:1–30

    MATH  Google Scholar 

Download references

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

Authors
  1. Jianping Gou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangshuo Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongwei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lan Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shaoning Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yunhao Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Weihua Ou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jianping Gou or Weihua Ou.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01461-y

Keywords

Search

Navigation