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Complete joint global and local collaborative marginal fisher analysis

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Abstract

Marginal Fisher analysis (MFA) maintains the nearest neighbor structure according to the class information of samples, so it achieves good recognition results for image recognition task. However, MFA needs to manually determine the number of nearest neighbor samples and simply sets the weight value of nearest neighbor samples to one. Furthermore, MFA only considers the local information and often encounters the small sample size problems while dealing with image recognition. Therefore, based on MFA and collaborative representation (CR), we introduce a new method, called a complete joint global and local collaborative marginal fisher analysis (CJGLCMFA). CJGLCMFA defines inter-class collaborative weighted matrix and intra-class collaborative weighted matrix based on CR and class label information, which is able to automatically construct the weighted matrices and avoid manually choosing nearest neighbors. In order to further enhance the algorithm, the objective function considers both local and global information of samples and then the complete solution for CJGLCMFA is given to solve the small sample size problem. Extensive experiments on some benchmark datasets show that CJGLCMFA is feasible and practicable, and the best recognition result of CJGLCMFA is 98.62%. Compared with other algorithms, the best improvement is 2.44% higher than the other best recognition results.

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References

  1. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Google Scholar 

  2. Zhang DG, Li WB, Liu S et al (2016) Novel fusion computing method for bio-medical image of WSN based on spherical coordinate. J Vibroengin 18(1):522–538

    Google Scholar 

  3. Ma Z, Zhang DG, Chen J et al (2016) Shadow detection of moving objects based on multisource information in Internet of things. J Exp Theore Artifi Intell 29(3):649–661

    Google Scholar 

  4. Yu W, Zhao C (2019) Online fault diagnosis in industrial processes using multimodel exponential discriminant analysis algorithm. IEEE Trans Control Syst Technol 27(3):1317–1325

    Google Scholar 

  5. Fulin L, Hong H, Yule D et al (2017) Local geometric structure feature for dimensionality reduction of hyperspectral imagery. Remote Sens 9(8):1–23

    Google Scholar 

  6. Shi G, Huang H, Wang L (2020) Unsupervised dimensionality reduction for hyperspectral imagery via local geometric structure feature learning. IEEE Geosci Remote Sens Lett 17(8):1425–1429

    Google Scholar 

  7. Wright J, Yang AY, Ganesh A et al Robust face recognition via sparse representation. IEEE Transac Patt Analy Mach Intell 31(2):210–227

  8. Zhang L, Yang M, Feng X (2012) Sparse representation or collaborative representation: which helps face recognition?[C]// international conference on computer vision. IEEE

  9. Gou J, Qiu W, Yi Z et al (2019) A Local Mean Representation-based K-Nearest Neighbor Classifier[J]. ACM Trans Intell Syst Technol 10(3):1–25

    Google Scholar 

  10. Han N, Wu J, Fang X et al (2020) Double relaxed regression for image classification[J]. IEEE Transac Circ Syst Video Technol 30(2):307–318

    Google Scholar 

  11. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Transac Patt Analy Mach Intell 19(7):711–720

    Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  14. He X, Cai D, Yan S, et al. (2005) Neighborhood preserving embedding. Tenth IEEE International Conference on Computer Vision, IEEE

  15. Yuan MD, Feng DZ, Shi Y et al (2019) Dimensionality reduction by collaborative preserving Fisher discriminant analysis. Neurocomputing 356(27):228–243

    Google Scholar 

  16. Han PY, Teoh ABJ, Abas FS (2012) Regularized locality preserving discriminant embedding for face recognition. Neurocomputing 77(1):156–166

    Google Scholar 

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

    Google Scholar 

  18. Wen J, Fang X, Cui J, Fei L, Yan K, Chen Y, Xu Y (2019) Robust sparse linear discriminant analysis[J]. IEEE Transac Circ Syst Video Technol 29(2):390–403

    Google Scholar 

  19. Li CN, Shao YH, Yin W, Liu MZ (2020) Robust and sparse linear discriminant analysis via an alternating direction method of multipliers[J]. IEEE Transac Neural Net Learn Syst 31(3):915–926

    Google Scholar 

  20. Zhang T, Huang K, Li X et al (2010) Discriminative orthogonal neighborhood preserving projections for classification. IEEE Transac Cyberne 40(1):253–263

    Google Scholar 

  21. Koringa P, Shikkenawis G, Mitra SK et al (2015) Modified Orthogonal Neighborhood Preserving Projection for Face Recognition. Lect Notes Comput Sci 9124:225–235

    Google Scholar 

  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 26(99):5019–5030

    MathSciNet  MATH  Google Scholar 

  23. Ning X, Li W, Tang B, He H (2018) BULDP: biomimetic uncorrelated locality discriminant projection for feature extraction in face recognition. IEEE Trans Image Process 27(5):1–10

    MathSciNet  MATH  Google Scholar 

  24. Xu D, Yan S, Tao D, Lin S, Zhang HJ (2007) Marginal fisher analysis and its variants for human gait recognition and content-based image retrieval. IEEE Trans Image Process 16(11):2811–2821

    MathSciNet  Google Scholar 

  25. Huang P, Li T, Gao G, Yang G (2019) Feature extraction based on graph discriminant embedding and its applications to face recognition. Soft Comput 23:7015–7028

    Google Scholar 

  26. Liu B, Zhou Y, Xia ZG et al (2018) Spectral regression based marginal Fisher analysis dimensionality reduction algorithm. Neurocomputing 277(14):101–107

    Google Scholar 

  27. Puthenputhussery A, Liu Q, Liu C (2017) A sparse representation model using the complete marginal fisher analysis framework and its applications to visual recognition. IEEE Transac MultI 19(8):1757–1770

    Google Scholar 

  28. Wan M, Lai Z (2017) Multi-manifold locality graph embedding based on the maximum margin criterion (MLGE/MMC) for face recognition. IEEE Access 5:9823–9830

    Google Scholar 

  29. Wan M, Lai Z, Yang G, Yang Z, Zhang F, Zheng H (2016) Local graph embedding based on maximum margin criterion via fuzzy set. Fuzzy Sets Syst 318:120–131

    MathSciNet  Google Scholar 

  30. Zhong F, Zhang J, Li D (2017) Discriminant locality preserving projections based on L1-norm maximization. IEEE Transac Neural Netw Learn Syst 25(11):2065–2074

    Google Scholar 

  31. Huang S, Zhuang L (2016) Exponential discriminant locality preserving projection for face recognition. Neurocomputing 208:373–377

    Google Scholar 

  32. Lu Y, Wu G (2020) Fast and incremental algorithms for exponential semi-supervised discriminant embedding. Pattern Recogn 108:107530

    Google Scholar 

  33. Chen WJ, Li CN, Shao YH et al (2019) 2DRLPP: Robust two-dimensional locality preserving projection with regularization. Knowl-Based Syst 169:53–66

    Google Scholar 

  34. Chen SB, Wang J, Liu CY et al (2019) Two-Dimensional Discriminant Locality Preserving Projection Based on L1-norm Maximization. Pattern Recogn Lett 87(1):147–154

    Google Scholar 

  35. Wan M, Yang G, Sun C, Liu M (2019) Sparse two-dimensional discriminant locality-preserving projection (S2DDLPP) for feature extraction. Soft Comput 189(23):5511–5518

    Google Scholar 

  36. Liang J, Chen C, Yi Y, Xu X, Ding M (2017) Bilateral two-dimensional neighborhood preserving discriminant embedding for face recognition. IEEE Access 5:17201–17212

    Google Scholar 

  37. Lou S, Zhao X, Chuang Y, Yu H, Zhang S (2016) Graph regularized sparsity discriminant analysis for face recognition. Neurocomputing 173(2):290–297

    Google Scholar 

  38. Wei L, Xu F, Wu A (2014) Weighted discriminative sparsity preserving embedding for face recognition. Knowl-Based Syst 57:136–145

    Google Scholar 

  39. Liu Z, Shi K, Zhang K, Ou W, Wang L (2020) Discriminative sparse embedding based on adaptive graph for dimension reduction. Eng Appl Artif Intell 94(9):103758

    Google Scholar 

  40. Chen N, Sui L, Zhang B et al (2021) Fusion of Hyperspectral-Multispectral images joining Spatial-Spectral Dual-Dictionary and structured sparse Low-rank representation. Int J Appl Earth Obs Geoinf 104(12):102570

    Google Scholar 

  41. Wen J, Han N, Fang X et al (2019) Low-Rank Preserving Projection Via Graph Regularized Reconstruction[J]. IEEE Transac Cybern 49(4):1279–1291

    Google Scholar 

  42. Lai Z, Mo D, Wong WK, Xu Y, Miao D, Zhang D (2018) Robust discriminant regression for feature extraction. IEEE transactions on. Cybernetics 48(8):2472–2484

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  44. Zhang L, Yang M, Feng X et al (2014) Collaborative representation based classification for face recognition. Comp ence 321:276–283

    Google Scholar 

  45. Chen GY, Krzyzak A, Xie WF (2021) Hyperspectral face recognition with histogram of oriented gradient features and collaborative representation-based classifier. Multimed Tools Tools App 81:2299–2310. https://doi.org/10.1007/s11042-021-11691-5

    Article  Google Scholar 

  46. Hua J, Wang H, Ren M, Huang H (2017) Collaborative representation analysis methods for feature extraction. Neural Comput Applic 28:225–231

    Google Scholar 

  47. Zang S, Wang C, Dong J (2019) A multilinear collaborative representation preserving projections method for feature extraction. J Comput Sci 30(1):48–54

    MathSciNet  Google Scholar 

  48. Yue M, Xiaohua W (2018) Discriminant sparse and collaborative preserving embedding for bearing fault diagnosis. Neurocomputing 313(3):259–270

    Google Scholar 

  49. Sharma M, Biswas M (2021) KLT-CRKCN: hyperspectral image classification via Karhunen Loeve transformation and collaborative representation-based K closest neighbor. Wirel Pers Commun 123:3347–3373. https://doi.org/10.1007/s11277-021-09292-4

    Article  Google Scholar 

  50. Ma P, Zhang H, Fan W (2017) Fault diagnosis of rolling bearings based on local and global preserving embedding algorithm. J Mechan Engin 53(2):20–25

    Google Scholar 

  51. Cai W (2016) A dimension reduction algorithm preserving both global and local clustering structure. Knowl-Based Syst 118:191–203

    Google Scholar 

  52. The ORL Face Database, http://www.uk.research.att.com/facedatabase.html

  53. The AR face database, http://rvl1.ecn.purdue.edu/aleix/aleixfaceH_DB.html

  54. Columbia University Image Library (COIL-20),http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php

  55. Chen L, Chen D, Yang F, Sun J (2021) A deep multi-task representation learning method for time series classification and retrieval[J]. Inf Sci 555:17–32

    MathSciNet  MATH  Google Scholar 

  56. An F-P, Ma X-M, Bai L (2022) Image fusion algorithm based on unsupervised deep learning-optimized sparse representation. Biomed Signal Proces Contr 71(Part B):103140. https://doi.org/10.1016/j.bspc.2021.103140

    Article  Google Scholar 

  57. Wu H, Qin S, Nie R et al (2021) Effective Collaborative Representation Learning for Multilabel Text Categorization [J]. IEEE Transac Neural Net Learn Syst PP(99):1–15

    Google Scholar 

  58. Thakur HK, Gupta A, Nag S, Shrivastava R (2020) Multi-class instance-incremental framework for classification in fully dynamic graphs[J]. Int J Comput Sci Eng 21(1):69–83

    Google Scholar 

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Acknowledgements

This research was supported by the Research Foundation of the Institute of Environment-friendly Materials and Occupational Health (Wuhu), Anhui University of Science and Technology (No. ALW2021YF04), the Science and Technology Research Project of Wuhu City (No. 2020yf48), the National Natural Science Foundation of China (No. 62076006) and the Major Science and Technology Projects in Anhui Province (No. 1803090102).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China

    Xingzhu Liang, Yu-e Lin, Shunxiang Zhang & Xianjin Fang

  2. Institute of Environment-friendly Materials and Occupational Health of Anhui University of Science and Technology (Wuhu), Wuhu, China

    Xingzhu Liang

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  1. Xingzhu Liang
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Correspondence to Xingzhu Liang.

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Liang, X., Lin, Ye., Zhang, S. et al. Complete joint global and local collaborative marginal fisher analysis. Appl Intell 53, 12873–12887 (2023). https://doi.org/10.1007/s10489-022-04125-8

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