Skip to main content
SpringerLink
Log in

Feature Extraction via Sparse Fuzzy Difference Embedding (SFDE) for Robust Subspace Learning

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Many classical feature extraction and dimensionality reduction algorithms, such as linear algorithm principal component analysis (PCA) and nonlinear algorithm local linear embedding (LLE), have been applied to face recognition. As we all know, PCA is a global algorithm and LLE is a local algorithm. However, PCA cannot obtain the local structure of high-dimensional spatial data samples, and LLE cannot obtain the global structure of high-dimensional spatial data samples. The application effect of these algorithms is not ideal, especially when they are always affected by overlapping points (outliers) and sparse points in the data. To solve these problems, the paper proposes a new effective feature extraction and dimension reduction algorithm called sparse fuzzy difference embedding (SFDE). Firstly, SFDE algorithm tries to search an optimal projection mapping matrix, which can affect not only the local of the fuzzy local minimizing embedding obtained by LLE but also the global of the fuzzy global maximizing variance obtained by PCA. Secondly, SFDE algorithm obtains the sparse transformation matrix by using the lasso regression return. This feature makes SFDE more intuitive and powerful than PCA, LLE, and other algorithms. Finally, we estimated the proposed algorithm through experiments in Yale and AR standard face databases and added the density of “salt and pepper” noise to the Yale and AR databases to verify the robustness of SFDE algorithm. The experimental results of the SFDE algorithm were better than those of the LDA, PCA, LLE, sparse differential embedding (SDE), and fuzzy local graph embedding based on maximum margin criterion algorithms because of its sparsity and fuzzy set, which also indicates that the SFDE algorithm is an effective algorithm.

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. Turk M, Pentland A (2011) Eigenfaces for recognition. Cognit Neurosci 3(1):71–86

    Article  Google Scholar 

  2. Swets D, Weng J (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8):831–836

    Article  Google Scholar 

  3. 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 

  4. Wan M, Chen X, Zhan T et al (2021) Sparse fuzzy two-dimensional discriminant local preserving projection (SF2DDLPP) for robust image feature extraction. Inf Sci 563:1–15

    Article  MathSciNet  Google Scholar 

  5. Zhao C, Wang X, Zuo W et al (2020) Similarity learning with joint transfer constraints for person re-identification. Pattern Recogn 97:107014

    Article  Google Scholar 

  6. Wan M, Lai Z, Yang G et al (2017) Local graph embedding based on maximum margin criterion via fuzzy set. Fuzzy Sets Syst 318:120–131

    Article  MathSciNet  Google Scholar 

  7. Fan Z, Zhang Da, Wang X, Zhu Qi, Wang Y-F (2018) Virtual dictionary based kernel sparse representation for face recognition. Pattern Recognit 76:1–13

    Article  Google Scholar 

  8. Wan M, Yang G, Sun C et al (2019) Sparse two-dimensional discriminant locality-preserving projection (S2DDLPP) for feature extraction. Soft Comput 23(14):5511–5518

    Article  Google Scholar 

  9. Liu Z, Wang J, Liu G et al (2019) Discriminative low-rank preserving projection for dimensionality reduction. Appl Soft Comput 85:105768

    Article  Google Scholar 

  10. Gao J, Li L, Guo B (2020) A new extendface representation method for face recognition. Neural Process Lett 51(1):473–486

    Article  Google Scholar 

  11. Gui-Fu Lu, Qin-Ru Yu, Wang Y, Tang G (2020) Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint. Neural Netw 125:214–223

    Article  Google Scholar 

  12. Zhang Z, Li F, Zhao M, Zhang Li, Yan S (2017) Robust neighborhood preserving projection by nuclear/L2,1-norm regularization for image feature extraction. IEEE Trans Image Process (IEEE TIP) 26(4):1607–1622

    Article  MathSciNet  Google Scholar 

  13. Tenenbaum J, DeSilva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  14. Belkin MP, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396

    Article  Google Scholar 

  15. Roweis S, Saul L (2000) Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  16. He X, Yan S, Hu Y, Zhang H (2003) Learning a locality preserving subspace for visual recognition. Proc Ninth Int Conf Comput Vis 1:385–392

    Google Scholar 

  17. He X, Yan S, Hu Y, Zhang NH (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  18. He K, Peng Y, Liu S et al (2020) Regularized negative label relaxation least squares regression for face recognition. Neural Process Lett 51(6):2629–2647

    Article  Google Scholar 

  19. Zhang Z, Zhang Y, Liu G, Tang J, Yan S, Wang M (2020) joint label prediction based semi-supervised adaptive concept factorization for robust data representation. IEEE Trans Knowl Data Eng (IEEE TKDE) 32(5):952–970

    Article  Google Scholar 

  20. Yang J, Zhang D, Yang JY, Niu B (2007) globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):650–664

    Article  Google Scholar 

  21. Zhang Z, Li F, Zhao M, Zhang Li, Yan S (2016) Joint low-rank and sparse principal feature coding for enhanced robust representation and visual classification. IEEE Trans Image Process 25(6):2429–2443

    Article  MathSciNet  Google Scholar 

  22. Yan S, Xu D, Zhang B et al (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29:40

    Article  Google Scholar 

  23. Zadeh LA (1965) Fuzzy sets. Inf ontrol 8:338–353

    Article  Google Scholar 

  24. Bezdek JC, Keller J, Krishnapuram R (1999) Fuzzy models and algorithms for pattern recognition and image processing. Kluwer Academic Publishers, Dordrecht

    Book  Google Scholar 

  25. Kw KC, Pedry W (2005) Face recognition using a fuzzy fisher classifier. Pattern Recogn 38(10):1717–1732

    Article  Google Scholar 

  26. Wan M, Li M, Lai Z et al (2012) Feature extraction based on fuzzy class mean embedding (FCME) with its application to face and palm biometrics. Mach Vis Appl 23(5):985–997

    Article  Google Scholar 

  27. Wan M, Yang G, Lai Z et al (2011) Feature extraction based on fuzzy local discriminant embedding with applications to face recognition. IET Comput Vision 5(5):301–308

    Article  MathSciNet  Google Scholar 

  28. Lai Z, Mo D, Wen J et al (2018) Generalized robust regression for jointly sparse subspace learning. IEEE Trans Circuits Syst Video Technol 29(3):756–772

    Article  Google Scholar 

  29. Liu Z, Lai Z, Ou W et al (2020) Structured optimal graph based sparse feature extraction for semi-supervised learning. Signal Process 170:107456

    Article  Google Scholar 

  30. Zhang Z, Jiang W, Qin J, Zhang Li, Li F, Zhang M, Yan S (2018) Jointly learning structured analysis discriminative dictionary and analysis multiclass classifier. IEEE Trans Neural Netw Learn Syst (IEEE TNNLS) 29(8):3798–3814

    Article  MathSciNet  Google Scholar 

  31. Wagner A, Wright J, Ganesh A et al (2012) Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans Pattern Anal Mach Intell 34(2):372–386

    Article  Google Scholar 

  32. Chen S, Cao J, Chen F et al (2020) Entropy-based fuzzy least squares twin support vector machine for pattern classification. Neural Process Lett 51(1):41–66

    Article  Google Scholar 

  33. Wan M, Lai Z (2017) Feature extraction via sparse difference embedding (SDE). KSII Trans Intern Inf Syst 11(7):3594–3607

    Google Scholar 

  34. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Royal Stat Soc Ser B 58:267–288

    MathSciNet  MATH  Google Scholar 

  35. Efron B, Hastie T, Johnstone I (2004) Least angle regression. Ann Stat 32(2):407–499

    Article  MathSciNet  Google Scholar 

  36. Zou H, Hastie T (2005) Regression shrinkage and selection via the elastic net, with applications to microarrays. J Royal Stat Soc Ser B Algorithmolo 67(2):301–320

    Article  Google Scholar 

  37. Imani M, Ghassemian H (2015) Ridge regression-based feature extraction for hyperspectral data. Int J Remote Sens 36(6):1728–1742

    Article  Google Scholar 

  38. Wickramasingha I, Elrewainy A, Sobhy M et al (2020) Tensor least angle regression for sparse representations of multidimensional signals. Neural Comput 32(4):1–36

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgement

This work is partially supported by the National Science Foundation of China under Grant Nos. 61876213, 6177227, 61861033, 61976118, U1831127, 71972102, the Key R&D Program Science Foundation in Colleges and Universities of Jiangshu Province Grant Nos.18KJA520005, 19KJA360001, 20KJA520002, the Natural Science Fund of Jiangsu Province under Grants Nos. BK20201397, BK20191409, BK20171494, the national Key R&D Program (Grant Nos.2017YFC0804002, 2019YF B1404602),the Natural Science Fund of Jiangxi Province under Grant No. 20202ACBL202007, the Natural Science Foundation of Guangdong Province under Grant No. 2016A030307050, and the Special Foundation of Public Research of Guangdong Province under Grant No. 2016A020225008 and Grant No. 2017A040405062.

Author information

Authors and Affiliations

  1. School of Information Engineeering, Nanjing Audit University, Nanjing, 211815, P. R. China

    Minghua Wan, Xichen Wang & Guowei Yang

  2. School of Computer and Information Engineering, Hanshan Normal University, Chaozhou, 521041, P. R. China

    Wei Huang

  3. Key Laboratory of Intelligent Information Processing, Nanjing Xiaozhuang University, Nanjing, 211171, P. R. China

    Hao Zheng

  4. School of Electronic Information, Qingdao University, Qingdao, 266071, P. R. China

    Guowei Yang

Authors
  1. Minghua Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xichen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guowei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Minghua Wan.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wan, M., Wang, X., Yang, G. et al. Feature Extraction via Sparse Fuzzy Difference Embedding (SFDE) for Robust Subspace Learning. Neural Process Lett 53, 2113–2128 (2021). https://doi.org/10.1007/s11063-021-10504-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-021-10504-y

Keywords

Search

Navigation