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Multi-view Intact Discriminant Space Learning for Image Classification

  • Xiwei Dong
  • Fei Wu
  • Xiao-Yuan Jing
  • Songsong Wu
Article
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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.

Keywords

Multi-view learning Subspace learning Fisher discrimination criterion Image classification Semi-supervised multi-view learning 

Notes

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.

References

  1. 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 2160–2167Google Scholar
  2. 2.
    Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7):2031–2038CrossRefGoogle Scholar
  3. 3.
    Kan M, Shan S, Zhang H, Lao S, Chen X (2016) Multi-view discriminant analysis. IEEE Trans Pattern Anal Mach Intell 38(1):188–194CrossRefGoogle Scholar
  4. 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):1635–1645CrossRefGoogle Scholar
  5. 5.
    Korn F, Pagel BU, Faloutsos C (2001) On the dimensionality curse and the self-similarity blessing. IEEE Trans Knowl Data Eng 13(1):96–111CrossRefGoogle Scholar
  6. 6.
    Wang W, Zhou ZH (2013) Co-training with insufficient views. In: Proceedings of the Asian conference on machine learning, pp 467–482Google Scholar
  7. 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 1968–1974Google Scholar
  8. 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 2750–2756Google Scholar
  9. 9.
    Gönen M, Alpaydın E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12(7):2211–2268MathSciNetzbMATHGoogle Scholar
  10. 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 1175–1182Google Scholar
  11. 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 1208–1216Google Scholar
  12. 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 1673–1681Google Scholar
  13. 13.
    Hardoon D, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664CrossRefGoogle Scholar
  14. 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 199–207Google Scholar
  15. 15.
    Xing X, Wang K, Yan T, Lv Z (2016) Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recognit 50:107–117CrossRefGoogle Scholar
  16. 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 370–385Google Scholar
  17. 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 982–990Google Scholar
  18. 18.
    Xia T, Tao D, Mei T, Zhang Y (2010) Multiview spectral embedding. IEEE Trans Syst Man Cybern Part B Cybern 40(6):1438–1446CrossRefGoogle Scholar
  19. 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 361–369Google Scholar
  20. 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 7–12Google Scholar
  21. 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):617–627CrossRefGoogle Scholar
  22. 22.
    Xu C, Tao D, Xu C (2015) Multi-view intact space learning. IEEE Trans Pattern Anal Mach Intell 37(12):2531–2544CrossRefGoogle Scholar
  23. 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):711–720CrossRefGoogle Scholar
  24. 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):757–763CrossRefGoogle Scholar
  25. 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):209–232MathSciNetCrossRefGoogle Scholar
  26. 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 1374–1381Google Scholar
  27. 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):1894–1904CrossRefGoogle Scholar
  28. 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):2005–2018CrossRefGoogle Scholar
  29. 29.
    Guan Z, Zhang L, Peng J, Fan J (2015) Multi-view concept learning for data representation. IEEE Trans Knowl Data Eng 27(11):3016–3028CrossRefGoogle Scholar
  30. 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):3111–3124CrossRefGoogle Scholar
  31. 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 701–708Google Scholar
  32. 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):3113–3127MathSciNetCrossRefGoogle Scholar
  33. 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):2966–2977CrossRefGoogle Scholar
  34. 34.
    Yi S, Lai Z, He Z, Cheung YM, Liu Y (2017) Joint sparse principal component analysis. Pattern Recognit 61:524–536CrossRefGoogle Scholar
  35. 35.
    Zhang P, You X, Ou W, Chen CP, Cheung YM (2016) Sparse discriminative multi-manifold embedding for one-sample face identification. Pattern Recognit 52:249–259CrossRefGoogle Scholar
  36. 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):354–364Google Scholar
  37. 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):1056–1068CrossRefGoogle Scholar
  38. 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 24–31Google Scholar
  39. 39.
    Xu X, Tsang IW, Xu D (2013) Soft margin multiple kernel learning. IEEE Trans Neural Netw Learn Syst 24(5):749–761CrossRefGoogle Scholar
  40. 40.
    Sonnenburg S, Rätsch G, Schäfer C, Schölkopf B (2006) Large scale multiple kernel learning. J Mach Learn Res 7(7):1531–1565MathSciNetzbMATHGoogle Scholar
  41. 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 775–782Google Scholar
  42. 42.
    Kloft M, Brefeld U, Sonnenburg S, Zien A (2011) Lp-norm multiple kernel learning. J Mach Learn Res 12(3):953–997MathSciNetzbMATHGoogle Scholar
  43. 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):4271–4285MathSciNetGoogle Scholar
  44. 44.
    Wang Q, Gu Y, Tuia D (2016) Discriminative multiple kernel learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 54(7):3912–3927CrossRefGoogle Scholar
  45. 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):6516–6530CrossRefGoogle Scholar
  46. 46.
    Shrivastava A, Patel VM, Chellappa R (2014) Multiple kernel learning for sparse representation-based classification. IEEE Trans Image Process 23(7):3013–3024MathSciNetCrossRefGoogle Scholar
  47. 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 92–100Google Scholar
  48. 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):2314–2324CrossRefGoogle Scholar
  49. 49.
    Wang W, Zhou ZH (2007) Analyzing co-training style algorithms. In: Proceedings of the European conference on machine learning, pp 454–465Google Scholar
  50. 50.
    Zhou ZH, Li M (2005) Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data Eng 17(11):1529–1541CrossRefGoogle Scholar
  51. 51.
    Li G, Chang K, Hoi SC (2012) Multiview semi-supervised learning with consensus. IEEE Trans Knowl Data Eng 24(11):2040–2051CrossRefGoogle Scholar
  52. 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):1279–1289CrossRefGoogle Scholar
  53. 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 63:229–245CrossRefGoogle Scholar
  54. 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 435–444Google Scholar
  55. 55.
    Zhang Z (1997) Parameter estimation techniques: a tutorial with application to conic fitting. Image Vis Comput 15(1):59–76CrossRefGoogle Scholar
  56. 56.
    Lu H, Plataniotis KN, Venetsanopoulos AN (2008) MPCA: multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39CrossRefGoogle Scholar
  57. 57.
    Aronszajn N (1950) Theory of reproducing kernels. Trans Am Math Soc 68(3):337–404MathSciNetCrossRefGoogle Scholar
  58. 58.
    Roth V, Steinhage V (1999) Nonlinear discriminant analysis using kernel functions. In: Proceedings of the conference on neural information processing systems, pp 568–574Google Scholar
  59. 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):59–70CrossRefGoogle Scholar
  60. 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 764–772Google Scholar
  61. 61.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRefGoogle Scholar
  62. 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 1817–1824Google Scholar
  63. 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 770–778Google Scholar
  64. 64.
    Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Proceedings of the European conference on computer vision, pp 818–833Google Scholar
  65. 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 248–255Google Scholar
  66. 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):211–252MathSciNetCrossRefGoogle Scholar
  67. 67.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of AutomationNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.State Key Laboratory of Software Engineering, School of ComputerWuhan UniversityWuhanChina
  3. 3.School of Information Science and TechnologyJiujiang UniversityJiujiangChina

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