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Sparsity Regularization Discriminant Projection for Feature Extraction

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Abstract

Recently, sparse representation models have attracted considerable interests in the field of feature extraction. In this paper, we propose a novel supervised feature extraction method called sparsity regularization discriminant projection (SRDP), which aims to preserve the sparse representation structure of the data and simultaneously maximize the ratio of nonlocal scatter to local scatter. More specifically, SRDP first constructs a concatenated dictionary through the class-wise principal component analysis decompositions. Second, the sparse representation structure of each sample is quickly learned with the constructed dictionary by matrix–vector multiplications. Then SRDP regards the learned sparse representation structure as an additional regularization term of unsupervised discriminant projection so as to construct a new discriminant function. Finally, SRDP is transformed into a generalized eigenvalue problem. Experimental results on five representative image databases demonstrate the effectiveness of our proposed method.

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References

  1. Wang Z, Yang W, Shen F (2016) Face recognition using a low rank representation based projections method. Adv Space Res 43(3):823–835

    Google Scholar 

  2. Yuan S, Mao X, Chen L (2017) Multilinear spatial discriminant analysis for dimensionality reduction. IEEE Trans Image Process 26(6):2669–2681

    Article  MathSciNet  MATH  Google Scholar 

  3. Nie F, Xiang S, Song Y, Zhang C (2009) Orthogonal locality minimizing globality maximizing projections for feature extraction. Opt Eng 48(1):017202

    Article  Google Scholar 

  4. Turk Matthew, Pentland Alex (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  5. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. 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 

  8. Wang G, Shi N, Shu Y et al (2016) Embedded manifold-based kernel Fisher discriminant analysis for face recognition. Neural Process Lett 43(1):1–16

    Article  Google Scholar 

  9. Lu GF, Zou J, Wang Y (2016) A new and fast implementation of orthogonal LDA algorithm and its incremental extension. Neural Process Lett 43(3):687–707

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  13. Bengio Y, Paiement JF, Vincent P et al (2004) Out-of-sample extensions for lle, isomap, mds, eigenmaps, and spectral clustering. Adv Neural Inf Process Syst 16:177–184

    Google Scholar 

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

    Article  Google Scholar 

  15. He X, Cai D,Yan S et al (2005) Neighborhood preserving embedding[C]//Computer Vision, 2005. In: ICCV 2005. Tenth IEEE international conference on IEEE, vol 2, pp 1208–1213

  16. Wang X, Liu Y, Nie F, Huang H (2015) Discriminative unsupervised dimensionality reduction. In: IJCAI, pp 3925–3931

  17. 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(1):40–51

    Article  Google Scholar 

  18. Nie F, Xu D, Tsang IWH, Zhang C (2010) Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction. IEEE Trans Image Process 19(7):1921–1932

    Article  MathSciNet  MATH  Google Scholar 

  19. 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 26(10):5019–5030

    Article  MathSciNet  MATH  Google Scholar 

  20. Wang R, Nie F, Yang X, Gao F, Yao M (2015) Robust 2DPCA with non-greedy L1-norm maximization for image analysis. IEEE Trans Cybern 45(5):1108–1112

    Article  Google Scholar 

  21. Yang J, Zhang D, Yang J et al (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  MathSciNet  Google Scholar 

  22. Nie F, Shiming X, Changshui Z (2007) Neighborhood MinMax Projections. In: IJCAI

  23. Zhang D, He J, Zhao Y et al (2014) Global plus local: a complete framework for feature extraction and recognition. Pattern Recogn 47(3):1433–1442

    Article  MATH  Google Scholar 

  24. Gao Q, Liu J, Zhang H et al (2013) Joint global and local structure discriminant analysis. IEEE Trans Inf Forensics Secur 8(4):626–635

    Article  Google Scholar 

  25. Zang F, Zhang J, Pan J (2012) Face recognition using elasticfaces. Pattern Recogn 45(11):3866–3876

    Article  MATH  Google Scholar 

  26. Luo T, Hou C, Yi D et al (2016) Discriminative orthogonal elastic preserving projections for classification. Neurocomputing 179:54–68

    Article  Google Scholar 

  27. Yuan S, Mao X (2018) Exponential elastic preserving projections for facial expression recognition. Neurocomputing 275:711–724

    Article  Google Scholar 

  28. Shojaeilangari S, Yau WY, Nandakumar K et al (2015) Robust representation and recognition of facial emotions using extreme sparse learning. IEEE Trans Image Process 24(7):2140–2152

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhang X, Pham DS, Venkatesh S et al (2015) Mixed-norm sparse representation for multi view face recognition. Pattern Recogn 48(9):2935–2946

    Article  Google Scholar 

  30. Liu Z, Pu J, Xu M et al (2015) Face recognition via weighted two phase test sample sparse representation. Neural Process Lett 41(1):43–53

    Article  Google Scholar 

  31. Qiao L, Chen S, Tan X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recogn 43(1):331–341

    Article  MATH  Google Scholar 

  32. Gui J, Sun Z, Jia W et al (2012) Discriminant sparse neighborhood preserving embedding for face recognition. Pattern Recogn 45(8):2884–2893

    Article  MATH  Google Scholar 

  33. Lei YK, Han H, Hao X (2015) Discriminant sparse local spline embedding with application to face recognition. Knowl Based Syst 89:47–55

    Article  Google Scholar 

  34. Yin F, Jiao LC, Shang F et al (2014) Sparse regularization discriminant analysis for face recognition. Neurocomputing 128:341–362

    Article  Google Scholar 

  35. Nie F, Huang H, Cai X, Ding CH (2010) Efficient and robust feature selection via joint L2, 1-norms minimization. In: Advances in neural information processing systems, pp 1813–1821

  36. Nie F, Wang H, Deng C, Gao X, Li X, Huang H (2016) New L1-norm relaxations and optimizations for graph clustering. In: AAAI, pp 1962–1968

  37. Deng C, Lv Z, Liu W, Huang J, Tao D, Gao X (2015) Multi-view matrix decomposition: a new scheme for exploring discriminative information. In: IJCAI, pp 3438–3444

  38. Nie F, Wang X, Huang H (2014) Clustering and projected clustering with adaptive neighbors. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 977–986

  39. The Olivetti & Oracle Research Laboratory Face Database of Faces. http://www.cam-orl.co.uk/facedatabase.html

  40. Sim T, Baker S, Bsat M (2002) The CMU pose, illumination, and expression (PIE) database[C]//Automatic Face and Gesture Recognition, 2002. In: Proceedings of the fifth IEEE international conference on IEEE, pp 46–51

  41. Phillips PJ, Wechsler H, Huang J et al (1998) The FERET database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16(5):295–306

    Article  Google Scholar 

  42. Huang GB et al (2007) Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Vol. 1. No. 2. Technical Report 07-49, University of Massachusetts, Amherst

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable and constructive criticisms that are very helpful to improve the quality of this paper. This work was supported by the National Science Foundation of China (Grant No. 61603013).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beihang University, Beijing, 100191, China

    Sen Yuan, Xia Mao & Lijiang Chen

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  1. Sen Yuan
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  2. Xia Mao
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  3. Lijiang Chen
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Correspondence to Lijiang Chen.

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Yuan, S., Mao, X. & Chen, L. Sparsity Regularization Discriminant Projection for Feature Extraction. Neural Process Lett 49, 539–553 (2019). https://doi.org/10.1007/s11063-018-9842-4

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