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Applied Intelligence

, Volume 49, Issue 2, pp 650–660 | Cite as

Multi-view uncorrelated discriminant analysis via dependence maximization

  • Xin ShuEmail author
  • Peisen Yuan
  • Haiyan Jiang
  • Darong Lai
Article
  • 92 Downloads

Abstract

This paper proposes a novel multi-view discriminant analysis based on Hilbert-Schmidt Independence Criterion (HSIC) and canonical correlation analysis (CCA). We use HSIC to identify a lower dimensional discriminant common subspace in which the dependence between multi-view features and the associated labels is maximized. CCA is utilized to achieve maximum correlation between different views in the common subspace. Motivated by the successful application of uncorrelated discriminant analysis, we further extend our approach to extract features with minimum redundancy. Experimental results validate the effectiveness of our proposed approaches.

Keywords

Hilbert-Schimdt independence criterion Feature extraction Multi-view discriminant analysis Canonical correlation analysis 

Notes

Acknowledgements

This work was supported by the Natural Science Foundation of China (Grants NO.61602248), the Natural Science Foundation of Jiangsu Province (Grants No. BK20160741) and the Fundamental Research Funds for the Central Universities (No.KJQN201733).

References

  1. 1.
    Amini M, Usunier N, Goutte C (2009) Learning from multiple partially observed views-an application to multilingual text categorization. In: Advances in neural information processing systems, pp 28–36Google Scholar
  2. 2.
    Cai D, He X, Han J, Zhang HJ (2006) Orthogonal laplacianfaces for face recognition. IEEE Trans Image Process 15(11):3608–3614CrossRefGoogle Scholar
  3. 3.
    Dhillon P, Foster DP, Ungar LH (2011) Multi-view learning of word embeddings via cca. In: Advances in neural information processing systems, pp 199–207Google Scholar
  4. 4.
    Diethe T, Hardoon DR, Shawe-Taylor J (2008) Multiview fisher discriminant analysis. In: NIPS workshop on learning from multiple sourcesGoogle Scholar
  5. 5.
    Gretton A, Bousquet O, Smola A, Schölkopf B (2005) Measuring statistical dependence with Hilbert-Schmidt norms. In: Algorithmic learning theory. Springer, pp 63–77Google Scholar
  6. 6.
    Gross R, Matthews I, Cohn J, Kanade T, Baker S (2010) Multi-pie. Image Vision Comput 28(5):807–813CrossRefGoogle Scholar
  7. 7.
    Han Y, Wu F, Tao D, Shao J, Zhuang Y, Jiang J (2012) Sparse unsupervised dimensionality reduction for multiple view data. IEEE Trans Circuits Syst Video Technol 22(10):1485–1496CrossRefGoogle Scholar
  8. 8.
    Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664CrossRefzbMATHGoogle Scholar
  9. 9.
    Hu P, Peng D, Guo J, Zhen L (2018) Local feature based multi-view discriminant analysis. Knowl-Based Syst 149:34–46CrossRefGoogle Scholar
  10. 10.
    Kan M, Shan S, Zhang H, Lao S, Chen X (2012) Multi-view discriminant analysis. In: ECCV 2012. Springer, pp 808–821Google Scholar
  11. 11.
    Lampert CH, Krömer O (2010) Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning. In: Computer vision–ECCV 2010. Springer, pp 566–579Google Scholar
  12. 12.
    Peng Y, Lu BL (2017) Discriminative extreme learning machine with supervised sparsity preserving for image classification. Neurocomputing 261:242–252CrossRefGoogle Scholar
  13. 13.
    Rasiwasia N, Costa Pereira J, Coviello E, Doyle G, Lanckriet GR, Levy R, Vasconcelos N (2010) A new approach to cross-modal multimedia retrieval. In: Proceedings of the international conference on multimedia. ACM, pp 251–260Google Scholar
  14. 14.
    Rupnik J, Shawe-Taylor J (2010) Multi-view canonical correlation analysis. In: Conference on data mining and data warehouses (siKDD 2010), pp 1–4Google Scholar
  15. 15.
    Sharma A, Kumar A, Daume H, Jacobs DW (2012) Generalized multiview analysis: a discriminative latent space. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2160–2167Google Scholar
  16. 16.
    Sigal L, Memisevic R, Fleet DJ (2009) Shared kernel information embedding for discriminative inference. In: IEEE conference on computer vision and pattern recognition. IEEE, pp 2852–2859Google Scholar
  17. 17.
    Sun QS, Zeng SG, Liu Y, Heng PA, Xia DS (2005) A new method of feature fusion and its application in image recognition. Pattern Recogn 38(12):2437–2448CrossRefGoogle Scholar
  18. 18.
    Sun S, Xie X, Yang M (2015) Multiview uncorrelated discriminant analysis. IEEE Trans Cybern PP(99):1–13Google Scholar
  19. 19.
    Sun T, Chen S (2007) Locality preserving cca with applications to data visualization and pose estimation. Image Vis Comput 25(5):531–543CrossRefGoogle Scholar
  20. 20.
    White M, Zhang X, Schuurmans D, Yu Yl (2012) Convex multi-view subspace learning. In: Advances in neural information processing systems, pp 1673–1681Google Scholar
  21. 21.
    Yang Y, Zhang W, Xie Y (2015) Image automatic annotation via multi-view deep representation. J Vis Commun Image Represent 33:368–377CrossRefGoogle Scholar
  22. 22.
    Ye J, Janardan R, Li Q, Park H (2006) Feature reduction via generalized uncorrelated linear discriminant analysis. IEEE Trans Knowl Data Eng 18(10):1312–1322CrossRefGoogle Scholar
  23. 23.
    Zhang Y, Zhou ZH (2010) Multilabel dimensionality reduction via dependence maximization. ACM Transactions on Knowledge Discovery from Data (TKDD) 4(3):14–34CrossRefGoogle Scholar
  24. 24.
    Zhao G, Pietikainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information Science and TechnologyNanjing Agricultural UniversityNanjingChina
  2. 2.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

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