Skip to main content
SpringerLink
Log in

Multi-view Intact Discriminant Space Learning for Image Classification

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Different views of one object usually represent different aspects of the object, and a single view is unlikely to comprehensively describe the object. In multi-view learning, comprehensive utilization of multi-view information is helpful. In this paper, we propose a novel supervised latent subspace learning method called multi-view intact discriminant space learning (MIDSL) by efficiently integrating complementary multi-view information of different views. MIDSL learns a latent intact discriminant space by employing Fisher discrimination criterion to fully use class label information, which can well guide exploiting useful discriminant information, of labeled training samples. MIDSL can simultaneously minimize the within-class scatter and maximize the between-class scatter of the feature representations of different objects in the learned latent intact discriminant space. Aiming to utilize unlabeled samples to help mining more useful information for better learning latent intact discriminant space, we extend MIDSL method in semi-supervised scenario and propose semi-supervised multi-view intact discriminant space learning (SMIDSL) method. We further extend MIDSL and SMIDSL methods by kernel technique and propose kernelized multi-view intact discriminant space learning (KMIDSL) and kernelized semi-supervised multi-view intact discriminant space learning (KSMIDSL) methods. Experimental results on Caltech 101, LFW, MNIST and RGB-D datasets demonstrate the effectiveness of our proposed methods.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Sharma A, Kumar A, Daume H, Jacobs DW (2012) Generalized multiview analysis: a discriminative latent space. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2160–2167

  2. Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7):2031–2038

    Article  Google Scholar 

  3. Kan M, Shan S, Zhang H, Lao S, Chen X (2016) Multi-view discriminant analysis. IEEE Trans Pattern Anal Mach Intell 38(1):188–194

    Article  Google Scholar 

  4. Jiang Y, Liu J, Li Z, Lu H (2014) Semi-supervised unified latent factor learning with multi-view data. Mach Vis Appl 25(7):1635–1645

    Article  Google Scholar 

  5. Korn F, Pagel BU, Faloutsos C (2001) On the dimensionality curse and the self-similarity blessing. IEEE Trans Knowl Data Eng 13(1):96–111

    Article  Google Scholar 

  6. Wang W, Zhou ZH (2013) Co-training with insufficient views. In: Proceedings of the Asian conference on machine learning, pp 467–482

  7. Li SY, Jiang Y, Zhou ZH (2014) Partial multi-view clustering. In: Proceedings of the conference on the AAAI conference on artificial intelligence, pp 1968–1974

  8. Li Y, Nie F, Huang H, Huang J (2015) Large-scale multi-view spectral clustering via bipartite graph. In: Proceedings of the conference on the AAAI conference on artificial intelligence, pp 2750–2756

  9. Gönen M, Alpaydın E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12(7):2211–2268

    MathSciNet  MATH  Google Scholar 

  10. Xu Z, Jin R, Yang H, King I, Lyu MR (2010) Simple and efficient multiple kernel learning by group lasso. In: Proceedings of the international conference on machine learning, pp 1175–1182

  11. Zhang D, He J, Liu Y, Si L Lawrence R (2011) Multi-view transfer learning with a large margin approach. In: Proceedings of the international conference on knowledge discovery and data mining, pp 1208–1216

  12. White M, Zhang X, Schuurmans D, Yu YL (2012) Convex multi-view subspace learning. In: Proceedings of the conference on neural information processing systems, pp 1673–1681

  13. Hardoon D, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664

    Article  MATH  Google Scholar 

  14. Dhillon P, Foster D, Ungar L (2011) Multi-view learning of word embeddings via CCA. In: Proceedings of the conference on neural information processing systems, pp 199–207

  15. Xing X, Wang K, Yan T, Lv Z (2016) Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recognit 50:107–117

    Article  Google Scholar 

  16. Viinikanoja J, Klami A, Kaski S (2010) Variational Bayesian mixture of robust CCA. In: Proceedings of the conference on the European conference on machine learning, pp 370–385

  17. Jia Y, Salzmann M, Darrell T (2010) Factorized latent spaces with structured sparsity. In: Proceedings of the conference on neural information processing systems, pp 982–990

  18. Xia T, Tao D, Mei T, Zhang Y (2010) Multiview spectral embedding. IEEE Trans Syst Man Cybern Part B Cybern 40(6):1438–1446

    Article  Google Scholar 

  19. Chen N, Zhu J, Xing EP (2010) Predictive subspace learning for multi-view data: a large margin approach. In: Proceedings of the conference on neural information processing systems, pp 361–369

  20. Xu C, Tao D, Li Y, Xu C (2013) Large-margin multi-view Gaussian process for image classification. In: Proceedings of the international conference on internet multimedia computing and service, pp 7–12

  21. Guo Y, Tao D, Liu W, Cheng J (2016) Multiview Cauchy estimator feature embedding for depth and inertial sensor-based human action recognition. IEEE Trans Syst Man Cybern Syst 47(4):617–627

    Article  Google Scholar 

  22. Xu C, Tao D, Xu C (2015) Multi-view intact space learning. IEEE Trans Pattern Anal Mach Intell 37(12):2531–2544

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Jensen CA, El-Sharkawi MA, Marks RJ (2001) Power system security assessment using neural networks: feature selection using Fisher discrimination. IEEE Trans Power Syst 16(4):757–763

    Article  Google Scholar 

  25. Yang M, Zhang L, Feng X, Zhang D (2014) Sparse representation based fisher discrimination dictionary learning for image classification. Int J Comput Vis 109(3):209–232

    Article  MathSciNet  MATH  Google Scholar 

  26. Jing XY, Wu F, Dong X, Shan S, Chen S (2017) Semi-supervised multi-view correlation feature learning with application to webpage classification. In: Proceedings of the AAAI conference on artificial intelligence, pp 1374–1381

  27. Shen X, Sun Q (2014) A novel semi-supervised canonical correlation analysis and extensions for multi-view dimensionality reduction. J Vis Commun Image Represent 25(8):1894–1904

    Article  Google Scholar 

  28. Chen X, Chen S, Xue H, Zhou X (2012) A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data. Pattern Recognit 45(5):2005–2018

    Article  MATH  Google Scholar 

  29. Guan Z, Zhang L, Peng J, Fan J (2015) Multi-view concept learning for data representation. IEEE Trans Knowl Data Eng 27(11):3016–3028

    Article  Google Scholar 

  30. Luo Y, Tao D, Ramamohanarao K, Xu C, Wen Y (2015) Tensor canonical correlation analysis for multi-view dimension reduction. IEEE Trans Knowl Data Eng 27(11):3111–3124

    Article  Google Scholar 

  31. Salzmann M, Ek CH, Urtasun R, Darrell T (2010) Factorized orthogonal latent spaces. In: Proceedings of the international conference on artificial intelligence and statistics, pp 701–708

  32. Li J, Xu C, Yang W, Sun C, Tao D (2017) Discriminative multi-view interactive image re-ranking. IEEE Trans Image Process 26(7):3113–3127

    Article  MathSciNet  MATH  Google Scholar 

  33. Yuan Y, Lin J, Wang Q (2016) Hyperspectral image classification via multitask joint sparse representation and stepwise MRF optimization. IEEE Trans Cybern 46(12):2966–2977

    Article  Google Scholar 

  34. Yi S, Lai Z, He Z, Cheung YM, Liu Y (2017) Joint sparse principal component analysis. Pattern Recognit 61:524–536

    Article  Google Scholar 

  35. Zhang P, You X, Ou W, Chen CP, Cheung YM (2016) Sparse discriminative multi-manifold embedding for one-sample face identification. Pattern Recognit 52:249–259

    Article  Google Scholar 

  36. He Z, Yi S, Cheung YM, You X, Tang YY (2017) Robust object tracking via key patch sparse representation. IEEE Trans Cybern 47(2):354–364

    Google Scholar 

  37. Gangeh MJ, Fewzee P, Ghodsi A, Kamel MS, Karray F (2014) Multiview supervised dictionary learning in speech emotion recognition. IEEE ACM Trans Audio Speech Lang Process 22(6):1056–1068

    Article  Google Scholar 

  38. Bennett KP, Momma M, Embrechts MJ (2002) MARK: a boosting algorithm for heterogeneous kernel models. In: Proceedings of the ACM international conference on knowledge discovery and data mining, pp 24–31

  39. Xu X, Tsang IW, Xu D (2013) Soft margin multiple kernel learning. IEEE Trans Neural Netw Learn Syst 24(5):749–761

    Article  Google Scholar 

  40. Sonnenburg S, Rätsch G, Schäfer C, Schölkopf B (2006) Large scale multiple kernel learning. J Mach Learn Res 7(7):1531–1565

    MathSciNet  MATH  Google Scholar 

  41. Rakotomamonjy A, Bach F, Canu S, Grandvalet Y (2007) More efficiency in multiple kernel learning. In: Proceedings of the international conference on machine learning, pp 775–782

  42. Kloft M, Brefeld U, Sonnenburg S, Zien A (2011) Lp-norm multiple kernel learning. J Mach Learn Res 12(3):953–997

    MathSciNet  MATH  Google Scholar 

  43. Zhang G, Sun H, Xia G, Sun Q (2016) Multiple kernel sparse representation-based orthogonal discriminative projection and its cost-sensitive extension. IEEE Trans Image Process 25(9):4271–4285

    MathSciNet  MATH  Google Scholar 

  44. Wang Q, Gu Y, Tuia D (2016) Discriminative multiple kernel learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 54(7):3912–3927

    Article  Google Scholar 

  45. Feng J, Jiao L, Sun T, Liu H, Zhang X (2016) Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection. IEEE Trans Geosci Remote Sens 54(11):6516–6530

    Article  Google Scholar 

  46. Shrivastava A, Patel VM, Chellappa R (2014) Multiple kernel learning for sparse representation-based classification. IEEE Trans Image Process 23(7):3013–3024

    Article  MathSciNet  MATH  Google Scholar 

  47. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the international conference on computational learning theory, pp 92–100

  48. You X, Peng Q, Yuan Y, Cheung YM, Lei J (2011) Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit 44(10–11):2314–2324

    Article  Google Scholar 

  49. Wang W, Zhou ZH (2007) Analyzing co-training style algorithms. In: Proceedings of the European conference on machine learning, pp 454–465

  50. Zhou ZH, Li M (2005) Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data Eng 17(11):1529–1541

    Article  Google Scholar 

  51. Li G, Chang K, Hoi SC (2012) Multiview semi-supervised learning with consensus. IEEE Trans Knowl Data Eng 24(11):2040–2051

    Article  Google Scholar 

  52. Wang Q, Lin J, Yuan Y (2016) Salient band selection for hyperspectral image classification via manifold ranking. IEEE Trans Neural Netw Learn Syst 27(6):1279–1289

    Article  Google Scholar 

  53. Appice A, Guccione P, Malerba D (2017) A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data. Pattern Recognit 63:229–245

    Article  Google Scholar 

  54. Wang J, Wang X, Tian F, Liu CH, Yu H, Liu Y (2016) Adaptive multi-view semi-supervised nonnegative matrix factorization. In: Proceedings of the international conference on neural information processing, pp 435–444

  55. Zhang Z (1997) Parameter estimation techniques: a tutorial with application to conic fitting. Image Vis Comput 15(1):59–76

    Article  Google Scholar 

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

    Article  Google Scholar 

  57. Aronszajn N (1950) Theory of reproducing kernels. Trans Am Math Soc 68(3):337–404

    Article  MathSciNet  MATH  Google Scholar 

  58. Roth V, Steinhage V (1999) Nonlinear discriminant analysis using kernel functions. In: Proceedings of the conference on neural information processing systems, pp 568–574

  59. Li F, Rob F, Pietro P (2007) Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput Vis Image Underst 106(1):59–70

    Article  Google Scholar 

  60. Huang G, Mattar M, Lee H Learned-Miller EG (2012) Learning to align from scratch. In: Proceedings of the conference on neural information processing systems, pp 764–772

  61. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  62. Lai K, Bo L, Ren X, Fox D (2011) A large-scale hierarchical multi-view RGB-D object dataset. In: Proceedings of the international conference on robotics and automation, pp 1817–1824

  63. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  64. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Proceedings of the European conference on computer vision, pp 818–833

  65. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 248–255

  66. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  67. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially funded by the National Science Foundation of China (NSFC) under Grant Numbers of 61272273 and 61702280. It is also supported by NSFC-Key Project of General Technology Fundamental Research United Fund (No. U1736211), Natural Science Foundation of Jiangsu Province (No. BK20170900), Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 17KJB520025), the Scientific Research Staring Foundation for Introduced Talents in NJUPT (NUPTSF, No. NY217009), Nanjing University of Posts and Telecommunications (No. XJKY14016). In addition, it is also funded by Education Department of Jiangxi Province under Grant No. GJJ151076.

Author information

Authors and Affiliations

  1. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Xiwei Dong, Fei Wu, Xiao-Yuan Jing & Songsong Wu

  2. State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China

    Xiao-Yuan Jing

  3. School of Information Science and Technology, Jiujiang University, Jiujiang, 332005, China

    Xiwei Dong

Authors
  1. Xiwei Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao-Yuan Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Songsong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiao-Yuan Jing.

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

Dong, X., Wu, F., Jing, XY. et al. Multi-view Intact Discriminant Space Learning for Image Classification. Neural Process Lett 50, 1661–1685 (2019). https://doi.org/10.1007/s11063-018-9951-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9951-0

Keywords

Search

Navigation