Skip to main content
SpringerLink
Log in

Semi-supervised dimension reduction approaches integrating global and local pattern information

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Dimension reduction is an important research area in pattern recognition. Use of both supervised and unsupervised data can be an advantage in the case of lack of labeled training data. Moreover, use of both global and local pattern information can contribute classification performances. Therefore, four important primary components are essential to design a well-performed semi-supervised dimension reduction approach: global pattern modeling by a supervised manner, local pattern modeling by a supervised manner, global pattern modeling by an unsupervised manner, and local pattern modeling by an unsupervised manner. These primary components are integrated into two proposed methods. The first is the semi-supervised global–local linear discriminant analysis, and the second is the semi-supervised global–local maximum margin criterion. The proposed methods are examined in object recognition and hyperspectral image classification. According to the experimental results, the promising results are obtained against to comparative semi-supervised methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, Hoboken (2001)

    MATH  Google Scholar 

  2. He, X., Niyogi, P.: Locality preserving projections. NIPS (2003)

  3. Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces:recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  4. Li, X., Jiang, T., Zhang, K.: Efficient and robust feature extraction by maximum margin criterion. IEEE Trans. Neural Netw. 17(1), 157–165 (2006)

    Article  Google Scholar 

  5. Zhang, D., He, J., Zhao, Y., Luo, Z., Du, M.: Global plus local: a complete framework for feature extraction and recognition. Pattern Recogn. 47(3), 1433–1442 (2014)

    Article  MATH  Google Scholar 

  6. Gao, Q., Liu, J., Zhang, H., Hou, J., Yang, X.: Enhanced fisher discriminant criterion for image recognition. Pattern Recogn. 45(10), 3717–3724 (2012)

    Article  Google Scholar 

  7. Sakarya, U.: Dimension reduction using global and local pattern information based maximum margin criterion. Signal Image Video Process. 10(5), 903–909 (2016)

    Article  Google Scholar 

  8. Lu, X., He, Z., Yi, S., Chen, W.-S.: Joint of locality- and globality—preserving projections. Signal Image Video Process. 12, 564–572 (2018)

    Google Scholar 

  9. Cai, D., He, X., Han, J.: Semi-supervised discriminant analysis. In: IEEE 11th International Conference on Computer Vision (2007)

  10. Song, Y., Nie, F., Zhang, C., Xiang, S.: A unified framework for semi-supervised dimensionality reduction. Pattern Recogn. 41(9), 2789–2799 (2008)

    Article  MATH  Google Scholar 

  11. Song, Y., Nie, F., Zhang, C.: Semi-supervised sub-manifold discriminant analysis. Pattern Recogn. Lett. 29(13), 1806–1813 (2008)

    Article  Google Scholar 

  12. Sugiyama, M., Ide, T., Nakajima, S., Sese, J.: Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Mach. Learn. 78, 35–61 (2010)

    Article  MathSciNet  Google Scholar 

  13. Huang, H., Li, J., Liu, J.: Enhanced semi-supervised local Fisher discriminant analysis for face recognition. Future Gener. Comput. Syst. 28, 244–253 (2012)

    Article  Google Scholar 

  14. Zhao, M., Chow, T.W.S., Wu, Z., Zhang, Z., Li, B.: Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction. Inf. Sci. 324, 286–309 (2015)

    Article  MATH  Google Scholar 

  15. Gao, S., Zhou, J., Yan, Y., Ye, Q.: Recursively global and local discriminant analysis for semi-supervised and unsupervised dimension reduction with image analysis. Neurocomputing 216, 672–683 (2016)

    Article  Google Scholar 

  16. Wang, N., Li, X., Chen, P.: Global and local scatter based semi-supervised dimensionality reduction with active constraints selection in ensemble subspaces. Pattern Anal. Appl. 20, 733–747 (2017)

    Article  MathSciNet  Google Scholar 

  17. Tu, W., Sun, S.: Semi-supervised feature extraction for EEG classification. Pattern Anal. Appl. 16, 213–222 (2013)

    Article  MathSciNet  Google Scholar 

  18. Mazgut, J., Tino, P., Boden, M., Yan, H.: Dimensionality reduction and topographic mapping of binary tensors. Pattern Anal. Appl. 17, 497–515 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  19. Zhang, S., Lei, Y., Zhang, C., Hu, Y.: Semi-supervised orthogonal discriminant projection for plant leaf classification. Pattern Anal. Appl. 19, 953–961 (2016)

    Article  MathSciNet  Google Scholar 

  20. Sakarya, U.: Hyperspectral dimension reduction using global and local information based linear discriminant analysis. In: ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. II–7, pp. 61–66 (2014)

  21. Nene, S., Nayar, S., Murase, H.: Columbia Object Image Library (COIL-100). http://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php.Access (1996). Accessed 27 Jan 2014

  22. AVIRIS NW Indiana’s Indian Pines 1992 data set. ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C.tif.zip (data). ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/ThyFiles.zip (ground truth). Access 07 May 2012

  23. Foody, G.M.: Status of land cover classification accuracy assessment. Remote Sens. Environ. 80(1), 185–201 (2002)

    Article  Google Scholar 

  24. R Core Team, R: A language and environment for statistical computing. http://www.R-project.org/, Vienna, Austria (2014)

  25. Hijmans, R.J.: Raster: geographic data analysis and modeling, R package version 2.3-24. http://CRAN.R-project.org/package=raster (2015)

  26. Bengtsson, H.: R.matlab: read and write ofMATfiles together with R-to-MATLAB connectivity, R package version 2.2.3. http://CRAN.R-project.org/package=R.matlab (2014)

  27. Beygelzimer, A., Kakadet, S., Langford, J., Arya, S., Mount, D., Li, S.: FNN: fast nearest neighbor search algorithms and applications, R package version 1.1. http://CRAN.R-project.org/package=FNN (2013)

  28. Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S, 4th edn. Springer, New York (2002)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TÜBİTAK UZAY (The Scientific and Technological Research Council of Turkey, Space Technologies Research Institute), ODTÜ Yerleşkesi, 06800, Ankara, Turkey

    Ufuk Sakarya

Authors
  1. Ufuk Sakarya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ufuk Sakarya.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sakarya, U. Semi-supervised dimension reduction approaches integrating global and local pattern information. SIViP 13, 171–178 (2019). https://doi.org/10.1007/s11760-018-1342-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1342-5

Keywords

Search

Navigation