Skip to main content
SpringerLink
Log in

An improved locality sensitive discriminant analysis approach for feature extraction

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Recently, Locality Sensitive Discriminant Analysis (LSDA) has been proposed as an efficient feature extraction approach. By analyzing the local manifold structure of high-dimensional data, LSDA can obtain a subspace in which the nearby points with the same label are close to each other while the nearby points with different labels are far apart. However, because LSDA only takes the local information into consideration, it may fail to deal with the data set which contains some outliers. In order to address this limitation, a new algorithm called Improved Locality Sensitive Discriminant Analysis (ILSDA) is proposed in this paper. By integrating the intra-class scatter matrix into our algorithm, ILSDA can not only preserve the local discriminant neighborhood structure of the data, but also pull the outlier samples more close to their class centers, which makes it outperform the original LSDA and some other state of the art algorithms. Extensive experimental results on several publicly available image datasets show the feasibility and effectiveness of our proposed approach.

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

Similar content being viewed by others

References

  1. Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inf Process Sys 14:585–591

    Google Scholar 

  2. Bengio Y, Paiement JF, Vincent P (2003) Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering. Advances in Neural Information Processing Systems. pp 177–184

  3. Cai D, He XF, Han J (2007) Isometric projection. In Proc. AAAI Conf. on Artificial Intelligence

  4. Cai D, He XF, Zhou K, et al. (2007) Locality sensitive discriminant analysis. In: Proceedings of 2007 International Joint Conference on Artificial Intelligence, Hyderabad, India

  5. Chen HT, Chang HW, Liu TL (2005) Local discriminant embedding and its variants. Proc IEEE Comput Soc Conf Comput Vis Pattern Recog 2:846–853

    Google Scholar 

  6. Chin TJ, Suter D (2008) Out-of-sample extrapolation of learned manifolds. IEEE Trans Pattern Anal Mach Intell 30(9):1547–1557

    Article  Google Scholar 

  7. Fu Y, Huang T (2005) Locally linear embedded eigenspace analysis. IFP-TR, Univ. of Illinois at Urbana-Champaign

  8. Gao QX, Liu JL, Zhang HJ, Hou J, Yang XJ (2012) Enhanced fisher discriminant criterion for image recognition. Pattern Recog 45(10):3717–3724

    Article  Google Scholar 

  9. Gui J, Jia W, Zhu L, Wang SL, Huang DS (2010) Locality preserving discriminant projections for face and palmprint recognition. Neurocomputing 73(13–15):2696–2707

    Article  Google Scholar 

  10. Gui J, Sun ZN, Jia W, Hu RX, Lei YK, Ji SW (2012) Discriminant sparse neighborhood preserving embedding for face recognition. Pattern Recog 45(8):2884–2893

    Article  MATH  Google Scholar 

  11. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: Data mining, inference and prediction. Springer, New York

  12. He XF, Cai D, Yan SC, Zhang HJ (2005) Neighborhood preserving embedding. In: Proceedings of IEEE international conference on computer vision, Beijing, China, pp 1208–1213

  13. He XF, Niyogi P (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  14. He XF, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using laplacian faces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  15. Hu H (2008) Orthogonal neighborhood preserving discriminant analysis for face recognition. Pattern Recog 41(6):2045–2054

    Article  MATH  Google Scholar 

  16. Hua Q, Bai LJ, Wang XZ, Liu YC (2012) Local similarity and diversity preserving discriminant projection for face and handwriting digits recognition. Neurocomputing 86(1):150–157

    Article  Google Scholar 

  17. Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37

    Article  Google Scholar 

  18. Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New York

    MATH  Google Scholar 

  19. Lee K, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698

    Article  Google Scholar 

  20. Li B, Huang DS, Wang C, Liu KH (2008) Feature extraction using constrained maximum variance mapping. Pattern Recog 41(11):3287–3294

    Article  MATH  Google Scholar 

  21. Li H, Jiang T, Zhang K (2006) Efficient and robust feature extraction by maximum margin criterion. IEEE Trans Neural Netw 17(1):1157–1165

    Article  Google Scholar 

  22. MartõÂnez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233

    Article  Google Scholar 

  23. Phillips PJ (2004) The facial recognition technology (FERET) database. http://www.itl.nist.gov/ad/humanid/feret/feret_master.html

  24. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104

    Article  Google Scholar 

  25. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  26. Tenenbaum JB, Silva VD, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2324

    Article  Google Scholar 

  27. Wang Y, Wu Y (2010) Complete neighborhood preserving embedding for face recognition. Pattern Recog 43(3):1008–1015

    Article  MATH  Google Scholar 

  28. Wang JZ, Zhang BX, Qi M, Kong J (2010) Linear discriminant projection embedding based on patches alignment. Image Vision Comput 28(12):1624–1636

    Article  Google Scholar 

  29. Wong WK, Zhao HT (2012) Supervised optimal locality preserving projection. Pattern Recog 45(1):186–197

    Article  MATH  Google Scholar 

  30. Yale University Face Database (2002) http://cvc.yale.edu/projects/yalefaces/yale-faces.html

  31. Yan SC, Xu D, Zhang BY et al (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  MathSciNet  Google Scholar 

  32. Yang G, Lai Z, Jin Z (2011) Feature extraction based on fuzzy local discriminant embedding with applications to face recognition. IET Comput Vis 5(5):301–308

    Article  MathSciNet  Google Scholar 

  33. Zhang HG, Deng WH, Guo J, Yang J (2010) Locality preserving and global discriminant projection with prior information. Mach Vis Appl 21(4):577–585

    Article  Google Scholar 

  34. Zhang TH, Yang J, Zhao DL, Ge XL (2007) Linear local tangent space alignment and application to face recognition. Neurocomputing 70(7–9):1547–1553

    Article  Google Scholar 

  35. Zhang Z, Zha H (2004) Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J Sci Comput 26(1):313–338

    Article  MATH  MathSciNet  Google Scholar 

  36. Zhao H, Sun S, Jing Z, Yang J (2006) Local structure based on supervised feature extraction. Pattern Recog 39(8):1546–1550

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by Fund of Jilin Provincial Science & Technology Department (No. 201115003, 20111804), Fundamental Research Funds for the Central Universities (No. 11QNJJ005), the Science Foundation for Post-doctor of Jilin Province (No. 2011274), Program for New Century Excellent Talents in University (NCET-09-0284), and the National Natural Science Foundation of China (No. 11271064).

Author information

Authors and Affiliations

  1. College of Computer Science and Information Technology, Northeast Normal University, Changchun, China

    Yugen Yi & Jun Kong

  2. Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China

    Jun Kong

  3. School of Mathematics and Statistics, Northeast Normal University, Changchun, China

    Yugen Yi & Baoxue Zhang

  4. National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun, China

    Jianzhong Wang

Authors
  1. Yugen Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baoxue Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianzhong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jun Kong or Jianzhong Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yi, Y., Zhang, B., Kong, J. et al. An improved locality sensitive discriminant analysis approach for feature extraction. Multimed Tools Appl 74, 85–104 (2015). https://doi.org/10.1007/s11042-013-1429-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1429-5

Keywords

Search

Navigation