KSII Transactions on Internet and Information Systems (TIIS)

  • 제12권9호
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  • Pages.4317-4335
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  • 2018
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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Semi-supervised Multi-view Manifold Discriminant Intact Space Learning

  • Han, Lu (College of Automation, Nanjing University of Posts and Telecommunications) ;
  • Wu, Fei (College of Automation, Nanjing University of Posts and Telecommunications) ;
  • Jing, Xiao-Yuan (College of Automation, Nanjing University of Posts and Telecommunications)
  • 투고 : 2017.12.07
  • 심사 : 2018.04.09
  • 발행 : 2018.09.30
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초록

Semi-supervised multi-view latent space learning is gaining considerable popularity recently in many machine learning applications due to the high cost and difficulty to obtain the large amount of label information of data. Although some semi-supervised multi-view latent space learning methods have been presented, there is still much space for improvement: 1) How to learn latent discriminant intact feature representations by employing data of multiple views; 2) How to exploit the manifold structure of both labeled and unlabeled point in the learned latent intact space effectively. To address the above issues, we propose an approach called semi-supervised multi-view manifold discriminant intact space learning ($SM^2DIS$) for image classification in this paper. $SM^2DIS$ aims to seek a manifold discriminant intact space for data of different views by making use of both the discriminant information of labeled data and the manifold structure of both labeled and unlabeled data. Experimental results on MNIST, COIL-20, Multi-PIE, and Caltech-101 databases demonstrate the effectiveness and robustness of our proposed approach.

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참고문헌

  1. M. Yang, and S. L. Sun, "Multi-view uncorrelated linear discriminant analysis with applications to handwritten digit recognition," in Proc. of the International Joint Conference on Neural Networks, pp. 4175-4181, July 6-11, 2014.
  2. S. L. Sun, X. J. Xie, and M. Yang, "Multi-view uncorrelated discriminant analysis," IEEE Transactions on Cybernetics, vol. 46, no. 12, pp. 3272-3284, 2016. https://doi.org/10.1109/TCYB.2015.2502248
  3. X. Y. Jing, Q. Liu, F. Wu, B. Xu, and Y. Zhu, "Web page classification based on uncorrelated semi-supervised intra-view and inter-view manifold discriminant feature extraction," in Proc. of the International Conference on Artificial Intelligence, pp. 2255-2261, July 25-31, 2015.
  4. M. Liu, Y. Luo, D. C. Tao, C. Xu, and Y. G. Wen, "Low-rank multi-view learning in matrix completion for multi-label image classification," in Proc. of the AAAI Conference on Artificial Intelligence, pp. 2778-2784, January 25-30, 2015.
  5. Z. Y. He, C. Chen, J. J. Bu, P. Li, and D. Cai, "Multi-view based multi-label propagation for image annotation," Neurocomputing, vol. 168, pp. 853-860, 2015. https://doi.org/10.1016/j.neucom.2015.05.039
  6. C. Deng, Z. T. Lv, W. Liu, J. Z. Huang, D. C. Tao, and X. B. Gao, "Multi-View Matrix Decomposition: A New Scheme for Exploring Discriminative Information," in Proc. of the International Conference on Artificial Intelligence, pp. 3438-3444, July 25-31, 2015.
  7. W. Y. Chang, C. P. Wei, and Y. C. F. Wang, "Multi-view nonnegative matrix factorization for clothing image characterization," in Proc. of the International Conference on Pattern Recognition, pp. 1272-1277, Augest 24-28, 2014.
  8. Sun S, "A survey of multi-view machine learning," Neural Computing and Applications, vol. 23, no. 7, pp. 2031-2038, 2013. https://doi.org/10.1007/s00521-013-1362-6
  9. Guo Y, "Convex subspace representation learning from multi-view data," in Proc. of the AAAI Conference on Artificial Intelligence, pp. 387-393, July 14-18, 2013.
  10. C. Zhang, Q. Hu, H. Fu, P. Zhu, and X. Cao, "Latent multi-view subspace clustering," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4333-4341, July 21-26, 2017.
  11. R. Nishant, N. Sumit, C. Santanu, and D. Om, "Partial Multi-view clustering using graph regularized NMF," in Proc. Of the International Conference on Pattern Recognition, pp. 2192-2197, December 4-8, 2016.
  12. Z. Xue, G. R. Li, S. H. Wang, W. G. Zhang, and Q. M. Huang, "Bi-Level multi-view latent space learning," IEEE Transactions on Circuits and Systems for Video Technology, In Press, pp. 1-14, 2016.
  13. C. Xu, D. C. Tao, and C. Xu, "Multi-view intact space learning," IEEE Transsactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 12, pp. 2531-2544, 2015. https://doi.org/10.1109/TPAMI.2015.2417578
  14. M. N. Kan, S. G. Shan, H. H. Zhang, S. H. Lao, and X. L. Chen, "Multi-view discriminant analysis," in Proc. of the European Conference on Computer Vision, pp. 808-821, October 7-13, 2012.
  15. M. N. Kan, S. G. Shan, H. H. Zhang, S. H. Lao, and X. L. Chen, "Multi-view discriminant analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 1, pp. 188-194, 2016. https://doi.org/10.1109/TPAMI.2015.2435740
  16. P. Yang, H. Davulcu, Y. D. Zhu, and J. R. He, "A generalized hierarchical multi-latent space model for heterogeneous learning," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 12, pp. 3154-3168, 2016. https://doi.org/10.1109/TKDE.2016.2611514
  17. J. Li, C. Xu, W. K. Yang, C. Y. Sun, and D. C. Tao, "Discriminative multi-view interactive image re-ranking," IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3113-3127, 2017. https://doi.org/10.1109/TIP.2017.2651379
  18. Q. J. Wang, H. Y. Lv, J. Yue, and E. Mitchell, "Supervised multiview learning based on simultaneous learning of multiview intact and single-view classifier," Neural Computing & Applications, vol. 28, no. 8, pp. 2293-2301, 2017. https://doi.org/10.1007/s00521-016-2189-8
  19. T. Diethe, D.R. Hardoon, and J. Shawe-Taylor, "Multi-view fisher discriminant analysis," in Proc. of NIPS Workshop on Learning from Multiple Sources, pp. 1-8, December 8-11, 2008.
  20. A. Sharma, A. Kumar, H. Daume, and D. W. Jacobs, "Generalized multiview analysis: a discriminative latent space," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2160-2167, June 16-21, 2012.
  21. Y. W. Guo, X. Q. Ding, and J. H. Xue, "MiLDA: a graph embedding approach to multi-view face recognition," Neurocomputing, vol. 151, no. 3, pp. 1255-1261, 2015. https://doi.org/10.1016/j.neucom.2014.11.004
  22. X. Y. Jing, F. Wu, X. W. Dong, S. G. Shan, and S. C. Chen, "Semi-supervised multi-view correlation feature learning with application to webpage classification," in Proc. of the AAAI Conference on Artificial Intelligence, pp. 1374-1381, February 4-9, 2017.
  23. H. Tao, C. P. Hou, F. P. Nie, J. B. Zhu, and D. Y. Yi, "Scalable multi-view semi-supervised classification via adaptive regression," IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4283-4296, 2017. https://doi.org/10.1109/TIP.2017.2717191
  24. F. Nie, G. Cai, and X. Li, "Multi-view clustering and semi-supervised classification with adaptive neighbors," in Proc. of the AAAI Conference on Artificial Intelligence, pp. 2408-2414, February 4-9, 2017.
  25. Y. Jiang, J. Liu, Z. Li, and H. Lu, "Semi-supervised unified latent factor learning with multi-view data," Machine Vision and Applications, vol. 25, no. 7, pp. 1635-1645, 2014. https://doi.org/10.1007/s00138-013-0556-3
  26. Z. Y. Guan, L. J. Zhang, J. Y. Peng, and J. P. Fan, "Multi-view concept learning for data representation," IEEE Transactions on Knowledge and Data Engineering, vol. 27 no. 11, pp. 3016-3028, 2015. https://doi.org/10.1109/TKDE.2015.2448542
  27. J. Liu, Y. Jiang, Z. C. Li, Z. H. Zhou, and H. Q. Lu, "Partially shared latent factor learning with multiview data," IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 6, pp. 1233-1246, 2015. https://doi.org/10.1109/TNNLS.2014.2335234
  28. J. Wang, X. Wang, F. Tian, C. H. Liu, H. Yu, and Y. Liu, "Adaptive multi-view semi-supervised nonnegative matrix factorization," in Proc. of the International Conference on Neural Information Processing, pp. 435-444, October 16-21, 2016.
  29. D. Cai, X. He, K. Zhou, J. Han, and H. Bao, "Locality sensitive discriminant analysis," in Proc. of the International Conference on Artificial Intelligence, pp. 714-719, January 6-12, 2007.
  30. I. Mizera, and C. H. Muller, "Breakdown points of Cauchy regresion-scale estimators," Statistics & Probability Letters, vol. 57, no. 1, pp. 79-82, 2002. https://doi.org/10.1016/S0167-7152(02)00057-3
  31. C. J. Lin, "Projected gradient methods for nonnegative matrix factorization," Neural Computation, vol. 19, no. 10, pp. 2756-2779, 2007. https://doi.org/10.1162/neco.2007.19.10.2756
  32. R. P. Wang, and X. L. Chen, "Manifold discriminant analysis," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 429-436, June 20-25, 2009.
  33. Y. M. Chen, and J. H. Chiang, "Face recognition using combined multiple feature extraction based on fourier-mellin approach for single example image per person," Pattern Recognition Letters, vol. 31, no. 13, pp. 1833-1841 , 2010. https://doi.org/10.1016/j.patrec.2010.03.018
  34. L. Han, X. Y. Jing, and F. Wu, "Multi-view local discriminantion and canonical correlation analysis for image classification," Neurocomputing, vol. 275C, pp. 1087-1098 , 2018.
  35. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324 , 1998. https://doi.org/10.1109/5.726791
  36. H. Murase, and S. K. Nayar, "Visual learning and recognition of 3-D objects from appearance," International Journal of Computer Vision, vol. 14, no. 1, pp. 5-24, 1995. https://doi.org/10.1007/BF01421486
  37. D. Cai, X. He, J. Han, and H. J. Zhang, "Orthogonal laplacian faces for face recognition," IEEE Transactions on Image Processing, vol. 15, no. 11, pp. 3608-3614, 2006. https://doi.org/10.1109/TIP.2006.881945
  38. F. F. Li, R. Fergus, and P. Perona, "One-shot learning of object categories," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 594-611, 2006. https://doi.org/10.1109/TPAMI.2006.79
  39. K. Fukunaga, and W. L. Koontz, "Application of the karhunen-loeve expansion to feature selection and ordering," IEEE Transactions on Computers, vol. 19, no. 4, pp. 311-318, 1970.
  40. S.E. Grigorescu, N. Petkov, and P. Kruizinga, "Comparison of texture features based on Gabor filters," IEEE Transactions on Image Processing, vol. 11, no. 10, pp. 1160-1167, 2002. https://doi.org/10.1109/TIP.2002.804262
  41. T. Ahonen, A. Hadid, and M. Pietikainen, "Face description with local binary patterns: application to face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006. https://doi.org/10.1109/TPAMI.2006.244
  42. A. Bosch, A. Zisserman, and X. Munoz, "Image classification using random forests and ferns," in Proc. of the International Conference on Computer Vision, pp. 1-8, October 14-21, 2007.
  43. H. Zhang, A. Berg, M. Maire, and J. Malik, "SVM-KNN: discriminative nearest-neighbor classification for visual category recognition," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2126-2136, June 17-22, 2006.
  44. E. Shechtman, and M. Irani, "Matching local self-similarities across image and videos," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, June 17-22, 2007.