Multi-View Learning with Incomplete Views

IEEE Transactions on Image Processing

Volumn 24, Issue 12, 2015, Pages 5812-5825

  Xu, Chang ^a   Tao, Dacheng ^b   Xu, Chao ^a  

^a Peking University   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

incomplete views; Multi view learning

Indexed keywords

ALGORITHMS; DATA HANDLING; OPTIMIZATION; VIRTUAL REALITY;

EFFECTIVE ALGORITHMS; INCOMPLETE VIEWS; LARGE-SCALE PROBLEM; MULTI-VIEW LEARNING; OBJECTIVE FUNCTIONS; OPTIMIZATION TECHNIQUES; SHARED SUBSPACES; SUCCESSIVE OVER RELAXATION METHODS;

LEARNING ALGORITHMS;

EID: 84953885329     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2015.2490539     Document Type: Article

References (53)

1. Z. Li and L. Itti, "Saliency and gist features for target detection in satellite images," IEEE Trans. Image Process., vol. 20, no. 7, pp. 2017-2029, Jul. 2011.
2. W. Zhang, J. Sun, X. Tang, "From tiger to panda: Animal head detection," IEEE Trans. Image Process., vol. 20, no. 6, pp. 1696-1708, Jun. 2011.
3. H. Yu, M. Li, H.-J. Zhang, J. Feng, "Color texture moments for content-based image retrieval," in Proc. Int. Conf. Image Process., vol. 3. Jun. 2002, pp. 929-932.
4. Z. Guo and D. Zhang, "A completed modeling of local binary pattern operator for texture classification," IEEE Trans. Image Process., vol. 19, no. 6, pp. 1657-1663, Jan. 2010.
5. B. Zhang, S. Shan, X. Chen, W. Gao, "Histogram of Gabor phase patterns (HGPP): A novel object representation approach for face recognition," IEEE Trans. Image Process., vol. 16, no. 1, pp. 57-68, Jan. 2007.
6. W. Zhang, S. Shan, W. Gao, X. Chen, H. Zhang, "Local Gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition," in Proc. 10th IEEE Int. Conf. Comput. Vis. (ICCV), vol. 1. Oct. 2005, pp. 786-791.
7. M. Kan, S. Shan, H. Zhang, S. Lao, X. Chen, "Multi-view discriminant analysis," in Computer Vision-ECCV. New York, NY, USA: Springer-Verlag, 2012, pp. 808-821.
8. H. Zhang, W. Gao, X. Chen, S. Shan, D. Zhao, "Robust multiview face detection using error correcting output codes," in Computer Vision-ECCV. New York, NY, USA: Springer-Verlag, 2006, pp. 1-12.
9. J. Yu, M. Wang, D. Tao, "Semisupervised multiview distance metric learning for cartoon synthesis," IEEE Trans. Image Process., vol. 21, no. 11, pp. 4636-4648, Nov. 2012.
10. J. Salvador and J. R. Casas, "Multi-view video representation based on fast Monte Carlo surface reconstruction," IEEE Trans. Image Process., vol. 22, no. 9, pp. 3342-3352, Sep. 2013.
11. C. Xu, D. Tao, C. Xu, "Multi-view self-paced learning for clustering," in Proc. 24th Int. Conf. Artif. Intell., 2015, pp. 3974-3980.
12. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training," in Proc. 11th Annu. Conf. Comput. Learn. Theory, 1998, pp. 92-100.
13. I. Muslea, S. Minton, C. A. Knoblock, "Active + semi-supervised learning = Robust multi-view learning," in Proc. ICML, vol. 2. 2002, pp. 435-442.
14. Z.-H. Zhou and M. Li, "Tri-training: Exploiting unlabeled data using three classifiers," IEEE Trans. Knowl. Data Eng., vol. 17, no. 11, pp. 1529-1541, Nov. 2005.
15. V. Sindhwani, P. Niyogi, M. Belkin, "A co-regularization approach to semi-supervised learning with multiple views," in Proc. Workshop Learn. Multiple Views ICML, 2005, pp. 1-6.
16. M.-F. Balcan, A. Blum, K. Yang, "Co-training and expansion: Towards bridging theory and practice," in Proc. NIPS, 2004, pp. 89-96.
17. W. Wang and Z.-H. Zhou, "A new analysis of co-training," in Proc. ICML, 2010, pp. 1135-1142.
18. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, M. I. Jordan, "Learning the kernel matrix with semidefinite programming," J. Mach. Learn. Res., vol. 5, pp. 27-72, Jan. 2004.
19. F. R. Bach, G. R. G. Lanckriet, M. I. Jordan, "Multiple kernel learning, conic duality, the SMO algorithm," in Proc. ICML, 2004, p. 6.
20. S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf, "Large scale multiple kernel learning," J. Mach. Learn. Res., vol. 7, pp. 1531-1565, Jan. 2006.
21. A. Rakotomamonjy, F. R. Bach, S. Canu, Y. Grandvalet, "SimpleMKL," J. Mach. Learn. Res., vol. 9, pp. 2491-2521, Nov. 2008.
22. C. Xu, D. Tao, C. Xu, "Multi-view intact space learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. PP, no. 99, p. 1, doi: 10.1109/TPAMI.2015.2417578.
23. K. Chaudhuri, S. M. Kakade, K. Livescu, K. Sridharan, "Multiview clustering via canonical correlation analysis," in Proc. ICML, 2009, pp. 129-136.
24. S. M. Kakade and D. P. Foster, "Multi-view regression via canonical correlation analysis," in Learning Theory. New York, NY, USA: Springer-Verlag, 2007, pp. 82-96.
25. S. Akaho. (2006). "A kernel method for canonical correlation analysis." [Online]. Available: http://arxiv.org/abs/cs/0609071
26. F. R. Bach and M. I. Jordan, "A probabilistic interpretation of canonical correlation analysis," Dept. Statist., Univ. California, Berkeley, Berkeley, CA, USA, Tech. Rep. TR 688, 2005.
27. A. Shon, K. Grochow, A. Hertzmann, R. P. Rao, "Learning shared latent structure for image synthesis and robotic imitation," in Proc. NIPS, 2005, pp. 1233-1240.
28. C. Xu, D. Tao, Y. Li, C. Xu, "Large-margin multi-view Gaussian process for image classification," in Proc. ICIMCS, 2013, pp. 7-12.
29. R. Memisevic, L. Sigal, D. J. Fleet, "Shared kernel information embedding for discriminative inference," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 778-790, Apr. 2012.
30. Y. Jia, M. Salzmann, T. Darrell, "Factorized latent spaces with structured sparsity," in Proc. NIPS, 2010, pp. 982-990.
31. N. Chen, J. Zhu, F. Sun, E. P. Xing, "Large-margin predictive latent subspace learning for multiview data analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 12, pp. 2365-2378, Dec. 2012.
32. C. Xu, D. Tao, C. Xu, "Large-margin multi-view information bottleneck," IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 8, pp. 1559-1572, Aug. 2014.
33. C. Xu, D. Tao, C. Xu. (2013). "A survey on multi-view learning." [Online]. Available: http://arxiv.org/abs/1304.5634
34. E. J. Candès and B. Recht, "Exact matrix completion via convex optimization," Found. Comput. Math., vol. 9, no. 6, pp. 717-772, Dec. 2009.
35. J. D. M. Rennie and N. Srebro, "Fast maximum margin matrix factorization for collaborative prediction," in Proc. 22nd Int. Conf. Mach. Learn., 2005, pp. 713-719.
36. R. H. Keshavan, A. Montanari, S. Oh, "Matrix completion from a few entries," IEEE Trans. Inf. Theory, vol. 56, no. 6, pp. 2980-2998, Jun. 2010.
37. P. Gong, J. Ye, C.-S. Zhang, "Multi-stage multi-task feature learning," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1988-1996.
38. A. Evgeniou and M. Pontil, "Multi-task feature learning," in Proc. Adv. Neural Inf. Process. Syst., vol. 19. 2007, p. 41.
39. X. Wang, J. Bi, S. Yu, J. Sun, "On multiplicative multitask feature learning," in Proc. Adv. Neural Inf. Process. Syst., 2014, pp. 2411-2419.
40. G. Michailidis and J. de Leeuw, "The Gifi system of descriptive multivariate analysis," Statist. Sci., vol. 34, no. 4, pp. 307-336, 1998.
41. J. de Leeuw and P. Mair, "Homogeneity analysis in R: The package homals," Dept. Statist., University of California, Los Angeles, CA, USA, Tech. Rep., 2007.
42. D. M. Strong, "Iterative methods for solving Ax = b," J. Online Math. Appl., 2006. [Online]. Available: http://www. math.nuk.edu.tw/jinnliu/teaching/na05/JM1.pdf
43. M. White, X. Zhang, D. Schuurmans, Y.-L. Yu, "Convex multiview subspace learning," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1673-1681.
44. D. Hardoon, S. Szedmak, J. Shawe-Taylor, "Canonical correlation analysis: An overview with application to learning methods," Neural Comput., vol. 16, no. 12, pp. 2639-2664, Dec. 2004.
45. Z. Wen, W. Yin, Y. Zhang, "Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm," Math. Program. Comput., vol. 4, no. 4, pp. 333-361, 2012.
46. S. Roth and M. J. Black, "Fields of experts," Int. J. Comput. Vis., vol. 82, no. 2, pp. 205-229, Apr. 2009.
47. M. F. Duarte and Y. H. Hu, "Vehicle classification in distributed sensor networks," J. Parallel Distrib. Comput., vol. 64, no. 7, pp. 826-838, Jul. 2004.
48. A. Frank et al., "UCI machine learning repository," 2010.
49. The FG-NET Aging Database. [Online]. Available: http://sting.cycollege.ac.cy/?alanitis/fgnetaging/index.htm
50. S. K. Zhou, B. Georgescu, X. S. Zhou, D. Comaniciu, "Image based regression using boosting method," in Proc. 10th IEEE Int. Conf. Comput. Vis. (ICCV), vol. 1. Oct. 2005, pp. 541-548.
51. K.-Y. Chang, C.-S. Chen, Y.-P. Hung, "Ordinal hyperplanes ranker with cost sensitivities for age estimation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2011, pp. 585-592.
52. A. Lanitis, C. J. Taylor, T. F. Cootes, "Toward automatic simulation of aging effects on face images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 4, pp. 442-455, Apr. 2002.
53. S. Ji, L. Sun, R. Jin, J. Ye, "Multi-label multiple kernel learning," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 777-784.