Skip to main content
Log in

Stable and orthogonal local discriminant embedding using trace ratio criterion for dimensionality reduction

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

Abstract

Stable orthogonal local discriminant embedding (SOLDE) is a recently proposed dimensionality reduction method, in which the similarity, diversity and interclass separability of the data samples are well utilized to obtain a set of orthogonal projection vectors. By combining multiple features of data, it outperforms many prevalent dimensionality reduction methods. However, the orthogonal projection vectors are obtained by a step-by-step procedure, which makes it computationally expensive. By generalizing the objective function of the SOLDE to a trace ratio problem, we propose a stable and orthogonal local discriminant embedding using trace ratio criterion (SOLDE-TR) for dimensionality reduction. An iterative procedure is provided to solve the trace ratio problem, due to which the SOLDE-TR method is always faster than the SOLDE. The projection vectors of the SOLDE-TR will always converge to a global solution, and the performances are always better than that of the SOLDE. Experimental results on two public image databases demonstrate the effectiveness and advantages of the proposed method.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Benmokhtar R, Delhumeau J, Gosselin P H (2013) Efficient supervised dimensionality reduction for image categorization. In: Proceedings of the 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2425–2428

  3. Boughnim N, Marot J, Fossati C, Bourennane S, Guerault F (2013) Fast and improved hand classification using dimensionality reduction and test set reduction. In: Proceedings of the 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1971–1975

  4. Cai D, He X, Han J, Zhang H (2006) Orthogonal laplacianfaces for face recognition. IEEE Trans Image Process 15(11):3608–3614

    Article  Google Scholar 

  5. Cai D, He X, Zhou K, Han J, Bao H (2007) Locality sensitive discriminant analysis. In: Proceedings of the 20th international joint conference on artificial intelligence, pp 708–713

  6. Chang X, Ma Z, Yang Y, Zeng Z, Hauptmann AG (2016) Bi-level semantic representation analysis for multimedia event detection. IEEE Trans Cybern 1–18. doi:10.1109/TCYB.2016.2539546

    Article  Google Scholar 

  7. Chang X, Nie F, Wang S, Yang Y, Zhou X, Zhang C (2016) Compound rank- k projections for bilinear analysis. IEEE Trans Neural Netw Learn Syst 27(7):1502–1513

    Article  MathSciNet  Google Scholar 

  8. Chang X, Yang Y (2016) Semisupervised feature analysis by mining correlations among multiple tasks. IEEE Trans Neural Netw Learn Syst 1–12. doi:10.1109/TNNLS.2016.2582746

    Article  MathSciNet  Google Scholar 

  9. Chang X, Yang Y, Long G, Zhang C, Hauptmann A G (2016) Dynamic concept composition for zero-example event detection. In: AAAI

  10. Chang X, Yu YL, Yang Y, Xing EP (2016) Semantic pooling for complex event analysis in untrimmed videos. IEEE Trans Pattern Anal Mach Intell (99). doi:10.1109/TCYB.2015.2479645

    Article  Google Scholar 

  11. Duchene J, Leclercq S (1988) An optimal transformation for discriminant and principal component analysis. IEEE Trans Pattern Anal Mach Intell 10(6):978–983

    Article  Google Scholar 

  12. Gao Q, Ma J, Zhang H, Gao X, Liu Y (2013) Stable orthogonal local discriminant embedding for linear dimensionality reduction. IEEE Trans Image Process 22(7):2521–2531

    Article  Google Scholar 

  13. Guo Y F, Li S J, Yang J Y, Shu T T, Wu L D (2003) A generalized foley–sammon transform based on generalized fisher discriminant criterion and its application to face recognition. Pattern Recogn Lett 24(1):147–158

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Jia Y, Nie F, Zhang C (2009) Trace ratio problem revisited. IEEE Trans Neural Netw 20(4):729–735

    Article  Google Scholar 

  16. Li B N, Yu Q, Wang R, Xiang K, Wang M, Li X (2016) Block principal component analysis with nongreedy 1-norm maximization. IEEE Trans Cybern 46(11):2543–2547

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Luo M, Chang X, Nie L, Yang Y, Hauptmann AG, Zheng Q (2017) An adaptive semisupervised feature analysis for video semantic recognition. IEEE Trans Cybern. doi:10.1109/TCYB.2017.2647904

    Article  Google Scholar 

  19. Parisotto E, Ghassabeh Y A, Freydoonnejad S, Rudzicz F (2015) Eeg dimensionality reduction in automatic identification of synonymy. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 847–851

  20. Wang H, Lu X, Hu Z, Zheng W (2014) Fisher discriminant analysis with L1-norm. IEEE Trans Cybern 44(6):828–842

    Article  Google Scholar 

  21. Wang H, Yan S, Xu D, Tang X, Huang T (2007) Trace ratio vs. ratio trace for dimensionality reduction. In: Proceedings of the 2007 IEEE conference on computer vision and pattern recognition, pp 1–8

  22. Wang R, Nie F, Hong R, Chang X, Yang X, Yu W (2017) Fast and orthogonal locality preserving projections for dimensionality reduction. IEEE Trans Image Process PP(99):1–1. ISSN 1057-7149. doi:10.1109/TIP.2017.2726188

    MathSciNet  MATH  Google Scholar 

  23. Wang S, Chen H, Peng X, Zhou C (2011) Exponential locality preserving projections for small sample size problem. Neurocomputing 74(17):3654–3662

    Article  Google Scholar 

  24. Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  25. Zhang T, Huang K, Li X, Yang J, Tao D (2010) Discriminative orthogonal neighborhood-preserving projections for classification. IEEE Trans Syst Man Cyberns Part B: Cybern 40(1):253–263

    Article  Google Scholar 

  26. Zoidi O, Nikolaidis N, Pitas I (2014) Semi-supervised dimensionality reduction on data with multiple representations for label propagation on facial images. In: Proceedings of the 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 6019–6023

Download references

Acknowledgements

This paper is supported by National Natural Science Foundation of China (No.61401471 and No.61501471); General Financial from the China Postdoctoral Science Foundation (No.2014M552589) and Special Financial from the China Postdoctoral Science Foundation (No.2015T81114).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rong Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, X., Liu, G., Yu, Q. et al. Stable and orthogonal local discriminant embedding using trace ratio criterion for dimensionality reduction. Multimed Tools Appl 77, 3071–3081 (2018). https://doi.org/10.1007/s11042-017-5022-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5022-1

Keywords

Navigation