Joint local and statistical discriminant learning via feature alignment

  • Elahe Gholenji
  • Jafar TahmoresnezhadEmail author
Original Paper


Image processing has attracted increasing attention in recent researches to solve domain shift problem where machine learning algorithms are applied to sets of unseen images. Domain shift problem occurs when the training (source domain) and test (target domain) sets are collected in different environmental conditions but in related domains. In this way, the adaptation across data distributions of the source and target datasets are suggested as domain adaptation framework to overcome the performance degradation. In this paper, a novel domain adaptation method referred as joint local and statistical discriminant learning via feature alignment (LSA), is proposed to find a cross-domain subspace by matching cross-domain distribution shift and adapting the class structures of the local and statistical distributions across the source and target domains, during the dimensionality reduction. Specifically, LSA projects samples into an embedded subspace in which the distances across the samples from same class are minimized and the distances across samples from different classes are maximized, at each local and statistical area, during alignment of marginal and conditional distributions. Furthermore, the class densities of samples based on manifold structure in different classes are preserved to provide more separability across various classes. To evaluate the proposed method, comprehensive experiments have been conducted on benchmark cross-domain object and digit recognition datasets. Experimental results have verified the superiority of LSA with a large margin in average classification accuracy against several state-of-the-art distribution matching and discriminant learning methods of domain adaptation. Moreover, the results have demonstrated the effectiveness of our proposed representation learning. Our source code is available at


Visual domain adaptation Discriminative feature learning Statistical structure Local structure Distribution matching 



  1. 1.
    Jhuo, I.H., Liu, D., Lee, D.T., Chang, S.F.: Robust visual domain adaptation with low-rank reconstruction. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, pp. 2168–2175. IEEEGoogle Scholar
  2. 2.
    Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: European conference on computer vision, September, pp. 213–226. Springer, Berlin, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)CrossRefGoogle Scholar
  4. 4.
    Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2200–2207 (2013)Google Scholar
  5. 5.
    Tahmoresnezhad, J., Hashemi, S.: Visual domain adaptation via transfer feature learning. Knowl. Inf. Syst. 50(2), 585–605 (2017)CrossRefGoogle Scholar
  6. 6.
    Fan, Y., Yan, G., Li, S., Song, S., Wang, W., Peng, X.: Transfer domain class clustering for unsupervised domain adaptation. In: International Conference on Electrical and Information Technologies for Rail Transportation, October, pp. 827–835. Springer, Singapore (2017)Google Scholar
  7. 7.
    Luo, L., Wang, X., Hu, S., Wang, C., Tang, Y., Chen, L.: Close yet distinctive domain adaptation (2017). arXiv preprint arXiv:1704.04235
  8. 8.
    Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: 2017 IEEE International Conference on Data Mining (ICDM), November, pp. 1129–1134. IEEE (2017)Google Scholar
  9. 9.
    Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation (2017). arXiv preprint arXiv:1705.05498
  10. 10.
    Liu, J., Li, J., Lu, K.: Coupled local-global adaptation for multi-source transfer learning. Neurocomputing 275, 247–254 (2018)CrossRefGoogle Scholar
  11. 11.
    Luo, L., Chen, L., Hu, S., Lu, Y., Wang, X.: Discriminative and geometry aware unsupervised domain adaptation (2017). arXiv preprint arXiv:1712.10042
  12. 12.
    Gretton, A., Borgwardt, K., Rasch, M.J., Scholkopf, B., Smola, A.J.: A kernel method for the two-sample problem (2008). arXiv:0805.2368
  13. 13.
    Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433–459 (2010)CrossRefGoogle Scholar
  14. 14.
    Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7(Nov), 2399–434 (2006)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Cai, D., He, X., Zhou, K., Han, J., Bao, H.: Locality sensitive discriminant analysis. In: IJCAI, January, pp. 1713–1726 (2007)Google Scholar
  16. 16.
    Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: a deep convolutional activation feature for generic visual recognition. In: International conference on machine learning, January, pp. 647–655 (2014)Google Scholar
  17. 17.
    Li, Y., Cheng, L., Peng, Y., Wen, Z., Ying, S.: Manifold alignment and distribution adaptation for unsupervised domain adaptation. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), July, pp. 688–693. IEEE (2019)Google Scholar
  18. 18.
    Li, J., Lu, K., Huang, Z., Zhu, L., Shen, H.T.: Transfer independently together: a generalized framework for domain adaptation. IEEE Trans. Cybern. 49(6), 2144–2155 (2018)CrossRefGoogle Scholar
  19. 19.
    Ghifary, M., Balduzzi, D., Kleijn, W.B., Zhang, M.: Scatter component analysis: a unified framework for domain adaptation and domain generalization. IEEE Trans. Pattern Anal. Mach. Intell. 39(7), 1414–30 (2017)CrossRefGoogle Scholar
  20. 20.
    Luo, L., Wang, X., Hu, S., Chen, L.: Robust data geometric structure aligned close yet discriminative domain adaptation (2017). arXiv:1705.08620
  21. 21.
    Liang, J., He, R., Sun, Z., Tan, T.: Aggregating randomized clustering-promoting invariant projections for domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 41(5), 1027–1042 (2018)CrossRefGoogle Scholar
  22. 22.
    Li, S., Song, S., Huang, G., Ding, Z., Wu, C.: Domain invariant and class discriminative feature learning for visual domain adaptation. IEEE Trans. Image Process. 27(9), 4260–4273 (2018)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)Google Scholar
  24. 24.
    Lu, H., Zhang, L., Cao, Z., Wei, W., Xian, K., Shen, C., van den Hengel, A.: When unsupervised domain adaptation meets tensor representations. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 599–608 (2017)Google Scholar
  25. 25.
    Gholami, B., Pavlovic, V.: Punda: Probabilistic unsupervised domain adaptation for knowledge transfer across visual categories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3581–3590 (2017)Google Scholar
  26. 26.
    Uzair, M., Mian, A.: Blind domain adaptation with augmented extreme learning machine features. IEEE Trans. Cybern. 47(3), 651–660 (2016)CrossRefGoogle Scholar
  27. 27.
    Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: maximizing for domain invariance (2014). arXiv preprint arXiv:1412.3474
  28. 28.
    Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks (2015). arXiv preprint arXiv:1502.02791

Copyright information

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

Authors and Affiliations

  1. 1.Faculty of IT and Computer EngineeringUrmia University of TechnologyUrmiaIran

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