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A multilinear unsupervised discriminant projections method for feature extraction

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Abstract

Despite considering the distribution information of data, unsupervised discriminant projection (UDP) ignores the space structure information of data for high order tensor objects. To address these problems, many tensor methods are developed for charactering the space structure information. Albeit effective, these methods ignore the local manifold structure of the samples, and thus achieve sub-optimal performance. In this paper, we formulate UDP in a high order tensor space and develop a Multilinear UDP (MUDP) for feature extraction on tensor objects. MUDP inherits the merits of UDP and Tensor based methods. The experiments tell that MUDP is an efficient and effective method and works well.

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References

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  3. Chen LF, Liao HYM, Ko MT, Yu GJ (2000) A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recogn 33(1):1713–1726

    Article  Google Scholar 

  4. Gu B, Sheng VS (2016) A robust regularization path algorithm for v-support vector classification. IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2016.2527796

    Article  Google Scholar 

  5. Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Transactions on Neural Networks and Learning Systems 26(7):1403–1416

    Article  MathSciNet  Google Scholar 

  6. Hastie T, Tibshirani R, Buja A (1994) Flexible discriminant analysis by optimal scoring. J Am Stat Assoc 89:1255–1270

    Article  MathSciNet  MATH  Google Scholar 

  7. He XF, Cai D, Niyogi P (2005a) Tensor subspace analysis. Advances in neural information processing systems, vol 18. Vancouver, Canada

    Google Scholar 

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

    Article  Google Scholar 

  9. Hong ZQ, Yang JY (1991) Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recogn 24(4):317–324

    Article  MathSciNet  Google Scholar 

  10. Jiang R, Lu R, Choo KKR (2016) Achieving high performance and privacy-preserving query over encrypted multidimensional big metering data. Futur Gener Comput Syst. doi:10.1016/j.future.2016.05.005

    Article  Google Scholar 

  11. Jin Z, Yang JY, Hu Z, Lou Z (2001) Face recognition based on the uncorrelated discrimination transformation. Patter Recognition 34(7):1405–1416

    Article  MATH  Google Scholar 

  12. Lai Z, Xu Y, Yang J, Tang J, Zhang D (2013) Sparse tensor discriminant analysis. IEEE Trans Image Process 22(10):3904–3915

    Article  MathSciNet  MATH  Google Scholar 

  13. Lai Z, Xu Y, Chen Q, Yang J, Zhang D (2014) Multilinear sparse principal component analysis. IEEE Trans on Neural Networks and Learning Systems 25(10):1942–1950

    Article  Google Scholar 

  14. Lu H, Plataniotis KN, Venetsanopoulos AN (2008) MPCA: multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39

  15. Lu J, Tan Y, Wang G (2013) Discriminative multimanifold analysis for face recognition from a single training sample per person. IEEE Trans Pattern Anal Mach Intell 35(1):39–51

    Article  Google Scholar 

  16. Phillips PJ (2004) The Facial Recognition Technology (FERET) Database. http://www.itl.nist.gov/iad/humanid/feret/feret_master.html

  17. 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 

  18. 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 

  19. Quick D, Choo KKR (2014a) Data reduction and data mining framework for digital forensic evidence: storage, intelligence, review and archive. Trends and Issues 480:1–11

    Google Scholar 

  20. Quick D, Choo KKR (2014b) Impacts of increasing volume of digital forensic data: a survey and future research challenges. Digit Investig 11:273–294

    Article  Google Scholar 

  21. Quick D, Choo KKR (2016a) Big forensic data management in heterogeneous distributed systems: quick analysis of multimedia forensic data. Softw Pract Exper. doi:10.1002/spe.2429

    Article  Google Scholar 

  22. Quick D, Choo KKR (2016b) Big forensic data reduction: digital forensic images and electronic evidence. Cluster Comput. doi:10.1007/s10586-016-0553-1

    Article  Google Scholar 

  23. Raudys SJ, Jain AK (1991) Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans Pattern Ana Mach Intell 13(3):252–264

    Article  Google Scholar 

  24. Ren C, Dai D (2010) Incremental learning of bidirectional principal components for face recognition. Pattern Recogn 43:318–330

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Tao DC, Li XL, Wu XD et al (2007a) General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29(10):1700–1715

    Article  Google Scholar 

  28. Tao DC, Li XL, Hu WM et al (2007b) Supervised tensor learning. Knowledge and Information Systems (Springer: KAIS) 2:1670–1677

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  32. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features [A]. In Proceedings of CVPR 2001: 511–518

  33. Wang L, Zhang J, Liu P, Choo KKR, Huang F (2016a) Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Comput. doi:10.1007/2Fs00500-016-2246-3

    Article  MATH  Google Scholar 

  34. Wang S, Yang M, Du S, Yang J, Liu B, Gorriz JM, Ramirez J, Yuan T-F, Zhang Y d (2016b) Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning. Front Comput Neurosci 10:Article ID: 160

    Google Scholar 

  35. Wang S, Lu S, Dong Z, Yang J, Yang M, Zhang Y (2016c) Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Applied Science 6(6):Article ID: 169

    Article  Google Scholar 

  36. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406

    Article  Google Scholar 

  37. Yan S, Xu D, Zhang B, Zhang H (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Ana1ysis and Machine Intelligence 29(1):40–51

    Article  Google Scholar 

  38. Yang J, Yang JY (2003) Why can LDA be performed in PCA transformed space? Pattern Recogn 36(2):563–566

    Article  Google Scholar 

  39. Yang J, Zhang D, Yang JY (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137

    Article  Google Scholar 

  40. Yang J, Zhang D, Yang J, 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 

  41. Yang W, Wang J, Ren M, Yang J (2009) Feature extraction based on laplacian bidirectional maximum margin criterion. Pattern Recogn 42(11):2327–2334

    Article  MATH  Google Scholar 

  42. Yang W, Sun C, Zhang L (2011) A multi-manifold discriminant analysis method for image feature extraction. Pattern Recogn 44(8):1649–1657

    Article  MATH  Google Scholar 

  43. Yang W, Wang Z, Sun C (2015) A collaborative representation based projections method for feature extraction. Pattern Recogn 48(1):20–27

    Article  Google Scholar 

  44. Yang W, Sun C, Zheng W, Ricanek K (2016a) Gender classification using 3D statistical models. Multimedia tools and applications:1–13. doi:10.1007/s11042-016-3446-7

    Article  Google Scholar 

  45. Yang W, Sun C, Zheng W (2016b) A regularized least square based discriminative projections for feature extraction. Neurocomputing 175:198–205

    Article  Google Scholar 

  46. Yu H, Yang J (2001) A direct LDA algorithm for high dimensional data--with application to face recognition. Pattern Recogn 34(10):2067–2070

    Article  MATH  Google Scholar 

  47. Yuan C, Sun X, Rui LV (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Communications 13(7):60–65

    Article  Google Scholar 

  48. Zhang DQ, Zhou ZZ (2005) (2D)2PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition. Neurocomputing 69(1–3):224–231

    Article  Google Scholar 

  49. Zhang Z, Liang Y, Bai L, Hancock ER (2016a) Discriminative sparse representation for face recognition. Multimedia Tools and Applications 75(7):3973–3992

    Article  Google Scholar 

  50. Zhang Y, Chen X, Zhan T, Jiao Z, Sun Y, Chen Z, Yao Y, Fang L-T, Lv Y-D, Wang S (2016b) Fractal dimension estimation for developing pathological brain detection system based on Minkowski-Bouligand method. IEEE Access 4:5937–5947

    Article  Google Scholar 

  51. Zhang Y, Peng B, Wang S, Liang Y, Yang J, So K-F, Yuan T-F (2016c) Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection. Sci Rep 6:21816

    Article  Google Scholar 

  52. Zhao H, Yuen P, Kwok J (2006) A novel incremental principal component analysis and its application for face recognition. IEEE Trans Syst Man Cybern Part B 36(4):873–886

    Article  Google Scholar 

  53. Zhao D, Lin Z, Tang X (2007) Laplacian PCA and its applications. ICCV 1–8

  54. Zuo WM, Zhang D, Wang K (2006a) Bidirectional PCA with assembled matrix distance metric for image recognition. IEEE Trans Syst Man Cybern Part B 36(4):862–872

    Google Scholar 

  55. Zuo WM, Zhang D, Yang J, Wang K (2006b) BDPCA plus LDA: a novel fast feature extraction technique for face Recognitino. IEEE Trans Syst Man Cybern Part B 36(4):946–952

    Article  Google Scholar 

Download references

Acknowledgement

This work is partly supported by Natural Science Foundation of China (61603190, 31671006) and the Natural Science Foundation of Jiangsu Province (No.BK20140638, BK2012437).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Haiyan Chen & Huan Wang

  2. Key Laboratory of Trusted Cloud Computing and Big Data Analysis, Nanjing Xiaozhuang University, Nanjing, 211171, China

    Haiyan Chen, Hao Zheng & Huan Wang

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Chengshan Qian

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  1. Haiyan Chen
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  2. Chengshan Qian
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  3. Hao Zheng
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  4. Huan Wang
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Correspondence to Huan Wang.

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Chen, H., Qian, C., Zheng, H. et al. A multilinear unsupervised discriminant projections method for feature extraction. Multimed Tools Appl 77, 3857–3870 (2018). https://doi.org/10.1007/s11042-016-4243-z

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