Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3857–3870 | Cite as

A multilinear unsupervised discriminant projections method for feature extraction

  • Haiyan Chen
  • Chengshan Qian
  • Hao Zheng
  • Huan Wang
Article
  • 153 Downloads

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.

Keywords

UDP Tensor Multilinear Feature extraction Face recognition 

Notes

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).

References

  1. 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):711–720CrossRefGoogle Scholar
  2. 2.
    Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396CrossRefMATHGoogle Scholar
  3. 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):1713–1726CrossRefGoogle Scholar
  4. 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 Google Scholar
  5. 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):1403–1416MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hastie T, Tibshirani R, Buja A (1994) Flexible discriminant analysis by optimal scoring. J Am Stat Assoc 89:1255–1270MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    He XF, Cai D, Niyogi P (2005a) Tensor subspace analysis. Advances in neural information processing systems, vol 18. Vancouver, CanadaGoogle Scholar
  8. 8.
    He X, Yan S, Hu Y, Niyogi P, Zhang H (2005b) Face recognition using laplacianfaces. IEEE Trans. Pattern Anal Mach Intell 27(3):328–340CrossRefGoogle Scholar
  9. 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):317–324MathSciNetCrossRefGoogle Scholar
  10. 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 Google Scholar
  11. 11.
    Jin Z, Yang JY, Hu Z, Lou Z (2001) Face recognition based on the uncorrelated discrimination transformation. Patter Recognition 34(7):1405–1416CrossRefMATHGoogle Scholar
  12. 12.
    Lai Z, Xu Y, Yang J, Tang J, Zhang D (2013) Sparse tensor discriminant analysis. IEEE Trans Image Process 22(10):3904–3915MathSciNetCrossRefMATHGoogle Scholar
  13. 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):1942–1950CrossRefGoogle Scholar
  14. 14.
    Lu H, Plataniotis KN, Venetsanopoulos AN (2008) MPCA: multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39Google Scholar
  15. 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):39–51CrossRefGoogle Scholar
  16. 16.
    Phillips PJ (2004) The Facial Recognition Technology (FERET) Database. http://www.itl.nist.gov/iad/humanid/feret/feret_master.html
  17. 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):1090–1104Google Scholar
  18. 18.
    Qiao L, Chen S, Tan X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recogn 43(1):331–341CrossRefMATHGoogle Scholar
  19. 19.
    Quick D, Choo KKR (2014a) Data reduction and data mining framework for digital forensic evidence: storage, intelligence, review and archive. Trends and Issues 480:1–11Google Scholar
  20. 20.
    Quick D, Choo KKR (2014b) Impacts of increasing volume of digital forensic data: a survey and future research challenges. Digit Investig 11:273–294CrossRefGoogle Scholar
  21. 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 Google Scholar
  22. 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 Google Scholar
  23. 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):252–264Google Scholar
  24. 24.
    Ren C, Dai D (2010) Incremental learning of bidirectional principal components for face recognition. Pattern Recogn 43:318–330CrossRefMATHGoogle Scholar
  25. 25.
    Roweis ST, Saul LK (2000) Nonlinear dimension reduction by locally linear embedding. Science 290:2323–2326CrossRefGoogle Scholar
  26. 26.
    Swets DL, Weng J (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8):831–836CrossRefGoogle Scholar
  27. 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):1700–1715CrossRefGoogle Scholar
  28. 28.
    Tao DC, Li XL, Hu WM et al (2007b) Supervised tensor learning. Knowledge and Information Systems (Springer: KAIS) 2:1670–1677Google Scholar
  29. 29.
    Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323CrossRefGoogle Scholar
  30. 30.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  31. 31.
    Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkCrossRefMATHGoogle Scholar
  32. 32.
    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features [A]. In Proceedings of CVPR 2001: 511–518Google Scholar
  33. 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 Google Scholar
  34. 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: 160Google Scholar
  35. 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: 169CrossRefGoogle Scholar
  36. 36.
    Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406CrossRefGoogle Scholar
  37. 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):40–51CrossRefGoogle Scholar
  38. 38.
    Yang J, Yang JY (2003) Why can LDA be performed in PCA transformed space? Pattern Recogn 36(2):563–566CrossRefGoogle Scholar
  39. 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):131–137CrossRefGoogle Scholar
  40. 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):650–664CrossRefGoogle Scholar
  41. 41.
    Yang W, Wang J, Ren M, Yang J (2009) Feature extraction based on laplacian bidirectional maximum margin criterion. Pattern Recogn 42(11):2327–2334CrossRefMATHGoogle Scholar
  42. 42.
    Yang W, Sun C, Zhang L (2011) A multi-manifold discriminant analysis method for image feature extraction. Pattern Recogn 44(8):1649–1657CrossRefMATHGoogle Scholar
  43. 43.
    Yang W, Wang Z, Sun C (2015) A collaborative representation based projections method for feature extraction. Pattern Recogn 48(1):20–27CrossRefGoogle Scholar
  44. 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
  45. 45.
    Yang W, Sun C, Zheng W (2016b) A regularized least square based discriminative projections for feature extraction. Neurocomputing 175:198–205CrossRefGoogle Scholar
  46. 46.
    Yu H, Yang J (2001) A direct LDA algorithm for high dimensional data--with application to face recognition. Pattern Recogn 34(10):2067–2070CrossRefMATHGoogle Scholar
  47. 47.
    Yuan C, Sun X, Rui LV (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Communications 13(7):60–65CrossRefGoogle Scholar
  48. 48.
    Zhang DQ, Zhou ZZ (2005) (2D)2PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition. Neurocomputing 69(1–3):224–231CrossRefGoogle Scholar
  49. 49.
    Zhang Z, Liang Y, Bai L, Hancock ER (2016a) Discriminative sparse representation for face recognition. Multimedia Tools and Applications 75(7):3973–3992CrossRefGoogle Scholar
  50. 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 4:5937–5947CrossRefGoogle Scholar
  51. 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 6:21816CrossRefGoogle Scholar
  52. 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):873–886CrossRefGoogle Scholar
  53. 53.
    Zhao D, Lin Z, Tang X (2007) Laplacian PCA and its applications. ICCV 1–8Google Scholar
  54. 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):862–872Google Scholar
  55. 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):946–952CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Haiyan Chen
    • 1
    • 2
  • Chengshan Qian
    • 3
  • Hao Zheng
    • 2
  • Huan Wang
    • 1
    • 2
  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.Key Laboratory of Trusted Cloud Computing and Big Data AnalysisNanjing Xiaozhuang UniversityNanjingChina
  3. 3.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

Personalised recommendations