Kernel Based Weighted Group Sparse Representation Classifier

  • Bingxin Xu
  • Ping Guo
  • C. L. Philip Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8008)


Sparse representation classification (SRC) is a new framework for classification and has been successfully applied to face recognition. However, SRC can not well classify the data when they are in the overlap feature space. In addition, SRC treats different samples equally and ignores the cooperation among samples belong to the same class. In this paper, a kernel based weighted group sparse classifier (KWGSC) is proposed. Kernel trick is not only used for mapping the original feature space into a high dimensional feature space, but also as a measure to select members of each group. The weight reflects the importance degree of training samples in different group. Substantial experiments on benchmark databases have been conducted to investigate the performance of proposed method in image classification. The experimental results demonstrate that the proposed KWGSC approach has a higher classification accuracy than that of SRC and other modified sparse representation classification.


Group sparse representation kernel method image classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wright, J., Yang, A.Y., Granesh, A.: Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)CrossRefGoogle Scholar
  2. 2.
    Yang, J.C., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation and raw patches. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  3. 3.
    Xu, B.X., Hu, R.K., Guo, P.: Combining affinity propagation with supervised dictionary learning for image classification. Neural Computing and Applications (in press), doi:10.1007/s00521-012-0957-7Google Scholar
  4. 4.
    Zou, H., Hastie, T.: Regularization and variable selection via the Elastic Net. Journal of the Royal Statistical Society, Series B 67(2), 301–320 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Majumdar, A., Ward, R.K.: Classification via group sparsity promoting regularization. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 861–864 (2009)Google Scholar
  6. 6.
    Majumdar, A., Ward, R.K.: Improved group sparse classifier. Pattern Recognition Letters 31(13), 1959–1964 (2010)CrossRefGoogle Scholar
  7. 7.
    Yin, J., Liu, Z.H., Jin, Z., Yang, W.K.: Kernel sparse representation based classification. Neurocomputing 77(1), 120–128 (2012)CrossRefGoogle Scholar
  8. 8.
    Zhang, L., Zhou, W.D., Chang, P.C., Liu, J., Yan, Z., Wang, T., Li, F.Z.: Kernel sparse representation-based classifier. IEEE Transactions on Signal Processing 60(4), 1684–1695 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Vapnik, V.N.: Statistical learning theory. Wiley-Interscience, New York (1998)zbMATHGoogle Scholar
  10. 10.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 2(2), 121–167 (1998)CrossRefGoogle Scholar
  11. 11.
    Kin, D.W., Lee, K.Y., Lee, L.K.H.: Evaluation of the performance of clustering algorithms in kernel-iinduced feature space. Patern Recoginition 38(4), 607–611 (2005)CrossRefGoogle Scholar
  12. 12.
    Graves, D., Pedrycz, W.: Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study. Fuzzy Sets and Systems 161(4), 522–543 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Scholkopf, B., Smola, A.J., Muller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299–1319 (1998)CrossRefGoogle Scholar
  14. 14.
    Chen, S.C., Zhang, D.Q.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics, part B: Cybernetics 34(4), 1907–1916 (2004)CrossRefGoogle Scholar
  15. 15.
    Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: Sarasota, F.S. (ed.) 2nd IEEE Workshop on Applications of Computer Vision, pp. 138–142 (1994)Google Scholar
  16. 16.
    Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)CrossRefGoogle Scholar
  17. 17.
    Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 684–698 (2005)CrossRefGoogle Scholar
  18. 18.
    Martinez, A., Benavente, R.: The AR face database. CVC Tech. Report. 24 (1998)Google Scholar
  19. 19.
    Deng, W., Yin, W.T., Zhang, Y.: Group sparse optimization by alternating direction method. Technical Report TR11-06, Department of Computational and Applied Mathematics, Rice University (2011)Google Scholar
  20. 20.
    Huang, J.Z., Zhang, T.: The benefit of group sparsity. Annals of Statistics 38(4), 1978–2004 (2010)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bingxin Xu
    • 1
  • Ping Guo
    • 1
  • C. L. Philip Chen
    • 2
  1. 1.Image Processing and Pattern Recognition LaboratoryBeijing Normal UniversityBeijingChina
  2. 2.Faculty of Science and TechnologyUniversity of MacauMacauChina

Personalised recommendations