Skip to main content
SpringerLink
Log in

Weighted marginal discriminant analysis

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper proposes a novel nonparametric discriminant analysis criterion, named weighted marginal discriminant analysis (WMDA), whose purpose is to efficiently utilize the marginal information of sample distribution in the discriminant analysis. The local mean is calculated by using the data points near the margin with different weights. The more contributions to the margin information, the larger weights the data points have. By making use of the weighting strategy and local mean, WMDA simultaneously utilizes the marginal information and local structure which are important for discriminative feature extraction. Experiments on the artificial and real database show that the proposed WMDA is superior to other related 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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

  2. http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html.

References

  1. Fisher Ronald A (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179–188

    Article  Google Scholar 

  2. Fukunaga K, Mantock JM (1983) Nonparametric discriminant analysis. TPAMI 5(6):671–678

    Article  MATH  Google Scholar 

  3. Bressan M, Vitrià J (2003) Nonparametric discriminant analysis and nearest neighbor classification. Pattern Recognit Lett 24(15):2743–2749

    Article  Google Scholar 

  4. Kuo B-C, Landgrebe David A (2004) Nonparametric weighted feature extraction for classification. Geosci Remote Sens 42:1096–1105

    Article  Google Scholar 

  5. Yan S-C, Xu D, Zhang B-Y, Zhang H-J, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. TPAMI 29:40–51

    Article  Google Scholar 

  6. Li Z-F, Lin D-H, Tang X-O (2009) Nonparametric discriminant analysis for face recognition. TPAMI 31(4):755–761

    Article  Google Scholar 

  7. He R, Hu BG, Yuan X-T (2009) Robust discriminant analysis based on nonparametric maximum entropy. Adv Mach Learn 5828:120–134

    Google Scholar 

  8. YangRan N, He R, Zheng W-S, Wang X (2012) Robust large margin discriminant tangent analysis for face recognition. Neural Comput Appl 21(2):1–11

    Google Scholar 

  9. He R, Sun Z, Tan T, Zheng W-S (2011) Recovery of corrupted low-rank matrices via half-quadratic based nonconvex minimization. In: CVPR 2889–2896. doi: 10.1109/CVPR.2011.5995328

  10. He R, Hu B-G, Yuan X, Zheng W-S (2010) Principal component analysis based on non-parametric maximum entropy. Neurocomputing 73:1840–1852

    Article  Google Scholar 

  11. He R, Hu B-G, Zheng W-S, Kong X-W (2011) Robust principal component analysis based on maximum correntropy criterion. TIP 20:1485–1494

    MathSciNet  Google Scholar 

  12. Qiu X, Wu L (2005) Face recognition by stepwise nonparametric margin maximum criterion. ICCV. IEEE 2:1567–1572

    Google Scholar 

  13. Gu Z, Yang J, Zhang L (2010) Push-pull marginal discriminant analysis for feature extraction. Pattern Recognit Lett 31(15):2345–2352

    Google Scholar 

  14. Zeng Q-S, Wang C-D (2010) NPDA/CS: improved non-parametric discriminant analysis with CS decomposition and its application to face recognition. In: ICIP 4537–4540

  15. Fukunaga K (1990) Statistical pattern recognition. Academic Press, San Diego

  16. Belhumeur Peter N, Hespanha Joäo P, Kriegman David J (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. TPAMI 19(7):711–720

    Article  Google Scholar 

  17. Dai DQ, Yuen PC (2003) Regularized discriminant analysis and its application to face recognition. Pattern Recognit 36:845–847

    Article  MATH  Google Scholar 

  18. Friedman J (1989) Regularized discriminant analysis. J Am Stat Assoc 84:165–175

    Article  Google Scholar 

  19. Price JR, Gee TF (2005) Face recognition using direct, weighted linear discriminant analysis and modular subspaces. Pattern Recogn Lett 38:209–219

    Article  MATH  Google Scholar 

  20. Zhao D, Lin Z, Xiao R, Tang X (2007) Linear Laplacian discrimination for feature extraction. In: CVPR 1–7

  21. Zheng W-S, Lai J-H, Yuen PC (2009) Perturbation LDA: learning the difference between the class empirical mean and its expectation. Pattern Recognit 42:764–679

    Article  MATH  Google Scholar 

  22. Jonathan JH (1994) A database for handwritten text recognition research. TPAMI 16:550–554

    Article  Google Scholar 

  23. Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. TPAMI 25(12):1615–1618

    Google Scholar 

Download references

Acknowledgments

The author would like to thank the anonymous reviewers for their critical and constructive comments and suggestions. This project was supported by the NSFC (61173084,51074097), GuangDong Program (2010B031000004) and GuangZhou Program (2011J4300046).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, 510006, People’s Republic of China

    Qingsong Zeng

  2. School of Information and Technology, Guangzhou Panyu Polytechnic, Guangzhou, 511483, People’s Republic of China

    Qingsong Zeng

Authors
  1. Qingsong Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingsong Zeng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zeng, Q. Weighted marginal discriminant analysis. Neural Comput & Applic 24, 503–511 (2014). https://doi.org/10.1007/s00521-012-1293-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-1293-7

Keywords

Search

Navigation