Improved Efficient Dictionary Learning with Cross-Label and Group Regularization

  • Tian Zhou
  • Sujuan Yang
  • Jian Xiong
  • Jie YangEmail author
  • Guan Gui
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)


Existing works have revealed that dictionary learning can effectively preserve the label property when applied to signal reconstruction and face recognition. As time goes, the methods based on dictionary learning become increasingly popular due to their superior accuracy and efficiency. Based on this, an improved dictionary learning model is proposed in this paper to find the balance between the time cost of operating the algorithms and the residuals generated when reconstructing signals with the learnt dictionary sparse codes. Demonstrated by the results of experiments, the proposed method intends to determine a more reasonable sparse dimension, which can not only obtain desired classification results but also decrease much of the redundancy in experimental computation.


Cross-label suppression Computational complexity Dictionary learning Face recognition Compressive sensing 


  1. 1.
    Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)CrossRefGoogle Scholar
  2. 2.
    Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision. Res. 37(23), 3311–3325 (1997)CrossRefGoogle Scholar
  3. 3.
    Li, Y., et al.: Sparse adaptive iteratively-weighted thresholding algorithm (SAITA) for Lp-regularization using the multiple sub-dictionary representation. Sens. 17(12), 2920–2936 (2017)CrossRefGoogle Scholar
  4. 4.
    Wang, F., Lee, N., Sun, J., Hu, J., Ebadollahi, S.: Automatic group sparse coding. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI). San Francisco, California, USA, 7–11 August 2011Google Scholar
  5. 5.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRefGoogle Scholar
  6. 6.
    Celebi, M.E., Kingravi, H.A., Vela, P.A.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40(1), 200–210 (2013)CrossRefGoogle Scholar
  7. 7.
    Rubinstein, R., Zibulevsky, M., Elad, M.: Double sparsity: learning sparse dictionaries for sparse signal approximation. IEEE Trans. Signal Process. 58(3), 1553–1564 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Zhang, Q., Li, B.: Discriminative K-SVD for dictionary learning in face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2691–2698 (2010)Google Scholar
  9. 9.
    Ramirez, I., Sprechmann, P., Sapiro, G.: Classification and clustering via dictionary learning with structured incoherence and shared features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3501–3508 (2010)Google Scholar
  10. 10.
    Wang, X., Gu, Y.: Cross-label suppression: a discriminative and fast dictionary learning with group regularization. IEEE Trans. Image Process. 26(8), 3859–3873 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lee, K.C., Ho, J., Kriegman, D.J.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)CrossRefGoogle Scholar
  12. 12.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 209–232 (1991)CrossRefGoogle Scholar
  13. 13.
    Jiang, Z., Lin, Z., Davis, L.S.: Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2651–2664 (2013)CrossRefGoogle Scholar
  14. 14.
    Wang, D., Kong, S.: A classification-oriented dictionary learning model: Explicitly learning the particularity and commonality across categories. Pattern Recognit. 47(2), 885–898 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Tian Zhou
    • 1
  • Sujuan Yang
    • 1
  • Jian Xiong
    • 1
  • Jie Yang
    • 1
    Email author
  • Guan Gui
    • 1
  1. 1.College of Telecommunication and Information Engineering, Nanjing University of Posts and TelecommunicationsNanjingChina

Personalised recommendations