Skip to main content
SpringerLink
Log in

A Novel Nonlinear Multi-feature Fusion Algorithm: Multiple Kernel Multiset Integrated Canonical Correlation Analysis

  • Conference paper
  • First Online:
Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11068))

Included in the following conference series:

  • 1476 Accesses

Abstract

Multiset integrated canonical correlation analysis (MICCA) can distinctly express the integral correlation among multi-group feature. Thus, MICCA is very powerful for multiple feature extraction. However, it is difficult to capture nonlinear relationships with the linear mapping. In order to overcome this problem, we, in this paper, propose a multi-kernel multiset integrated canonical correlation analysis (MK-MICCA) framework for subspace learning. In the MK-MICCA framework, the input data of each feature are mapped into multiple higher dimensional feature spaces by implicitly nonlinear mappings determined by different kernels. This enables MK-MICCA to uncover a variety of different geometrical structures of the original data in the feature spaces. Extensive experimental results on multiple feature database and ORL database show that MK-MICCA is very effective and obviously outperforms the single-kernel-based MICCA.

Supported by National Natural Science Foundation of China (61273251), Natural Science Foundation of Shandong Province (ZR2018BF010, ZR2016AM24) and Research Fund for the Doctoral Program (318051715).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16, 2639–2664 (2004)

    Article  Google Scholar 

  2. Sun, Q.-S., Zeng, S.-G., Liu, Y., Heng, P.-A., Xia, D.-S.: A new method of feature fusion and its application in image recognition. Pattern Recognit. 38, 2437–2448 (2005)

    Article  Google Scholar 

  3. Yuan, Y.H., et al.: Multiview correlation feature learning with multiple kernels. In: He, X., et al. (eds.) IScIDE 2015. LNCS, vol. 9243, pp. 518–528. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23862-3_51

    Chapter  Google Scholar 

  4. Hou, S.-D., Sun, Q.-S., Xia, D.-S.: Supervised locality preserving canonical correlation analysis algorithm. Pattern Recognit. Artif. Intell. 25(1), 143–149 (2012)

    Google Scholar 

  5. Melzer, T., Reiter, M., Bischof, H.: Appearance models based on kernel canonical correlation analysis. Pattern Recognit. 36, 1961–1971 (2003)

    Article  Google Scholar 

  6. Kidron, E., Schechner, Y.Y., Elad, M.: Pixels that Sound. IEEE Proc. Comput. Vis. Pattern Recognit. 1, 88–95 (2005)

    Google Scholar 

  7. Fortuna, B.: Kernel canonical correlation analysis with applications. In: SIKDD 2004 at Multiconference IS 2004, Ljubljana, Slovenia, pp. 12–15, October 2004

    Google Scholar 

  8. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge Press, Cambridge (2004)

    Book  Google Scholar 

  9. Sun, Q.-S., Liu, Z.-D., Heng, P.-A., Xia, D.-S.: A theorem on the generalized canonical projective vectors. Pattern Recognit. 38, 449–452 (2005)

    Article  Google Scholar 

  10. Sun, T., Chen, S., Yang, J., Shi, P.: A novel method of combined feature extraction for recognition. In: Eighth IEEE International Conference on Data Mining, ICDM08, pp. 1043–1048. IEEE (2008)

    Google Scholar 

  11. Sun, T., Chen, S.: Locality preserving CCA with applications to data visualization and pose estimation. Image Vis. Comput. 25, 531–543 (2007)

    Article  Google Scholar 

  12. Peng, Y., Zhang, D., Zhang, J.: A new canonical correlation analysis algorithm with local discrimination. Neural Process. Lett. 31, 1–15 (2010)

    Article  Google Scholar 

  13. Kettenring, J.R.: Canonical analysis of several sets of variables. Biometrika 58(3), 433–451 (1971)

    Article  MathSciNet  Google Scholar 

  14. Yuan, Y.-H., Sun, Q.-S., Zhou, Q., Xia, D.-S.: A novel multiset integrated canonical correlation analysis framework and its application in feature fusion. Pattern Recognit. 44, 1031–1040 (2011)

    Article  Google Scholar 

  15. Rupnik, J., Shawe-Taylor, J.: Multi-view canonical correlation analysis. In: Proceedings of Conference on Data Mining and Data Warehouses (2010). http://ailab.ijs.si/dunja/SiKDD2010/Papers/RupnikFinal.pdf

  16. Sonnenburg, S., Räatsch, G., Schäfer, C., Schölkopf, B.: Large scale multiple kernel learning. J. Mach. Learn. Res. 7, 1531–1565 (2006)

    MathSciNet  MATH  Google Scholar 

  17. Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y.: More efficiency in multiple kernel learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 775–782 (2007)

    Google Scholar 

  18. Xu, X., Tsang, I.W., Xu, D.: Soft margin multiple kernel learning. IEEE Trans. Neural Netw. Learn. Syst. 24(5), 749–761 (2013)

    Article  Google Scholar 

  19. Zhang, G.Q., Sun, H., Xia, G., Sun, Q.: Multiple kernel sparse representation based orthogonal discriminative projection and its cost-sensitive extension. IEEE Trans. Image Process. 25(9), 4271–4285 (2016)

    MathSciNet  Google Scholar 

  20. Kim, S.J., Magnani, A., Boyd, S.: Optimal kernel selection in kernel fisher discriminant analysis. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 465–472 (2006)

    Google Scholar 

  21. Yan, F., Kittler, J., Mikolajczyk, K., Tahir, A.: Nonsparse multiple kernel fisher discriminant analysis. J. Mach. Learn. Res. 13(1), 607–642 (2012)

    MathSciNet  MATH  Google Scholar 

  22. Lin, Y.Y., Liu, T.L., Fuh, C.S.: Multiple kernel learning for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 33(6), 1147–1160 (2011)

    Article  Google Scholar 

  23. Gan, H.: Multi-class semi-supervised kernel minimum squared error for face recognition. Opt. Int. J. Light. Electron Opt. 126(23), 3496–3500 (2015)

    Article  Google Scholar 

  24. Sun, Q.-S.: Research on Feature Extraction and Image Recognition Based on Correlation Projection Analysis, Ph.D. Dissertation, Nanjing University of Science and Technology, Nanjing (2006)

    Google Scholar 

  25. Sun, J.: An algorithm for the solution of multi-parameter eigenvalue problems (II). J. Comput. Math. 4, 354–363 (1986)

    MathSciNet  MATH  Google Scholar 

  26. Wang, Z., Chen, S., Sun, T.: MultiK-MHKS: a novel multiple kernel learning algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 348–353 (2008)

    Article  Google Scholar 

  27. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)

    Article  Google Scholar 

  28. Li, B., Huo, G.: Face recognition using locality sensitive histograms of oriented gradients. Opt. Int. J. Light. Electron Opt. 127(6), 3489–3494 (2016)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Liaocheng University, Liaocheng, 252059, China

    Jing Yang, Liya Fan & Yuhua Fan

  2. Nanjing University of Science and Technology, Nanjing, 210094, China

    Quansen Sun

Authors
  1. Jing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liya Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Quansen Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuhua Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Yang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, J., Fan, L., Sun, Q., Fan, Y. (2018). A Novel Nonlinear Multi-feature Fusion Algorithm: Multiple Kernel Multiset Integrated Canonical Correlation Analysis. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00021-9_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00020-2

  • Online ISBN: 978-3-030-00021-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation