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Research Article | Open Access

Learning multi-kernel multi-view canonical correlations for image recognition

Department of Computer Science, College of Information Engineering, Yangzhou University, Yangzhou 225127, China.
Department of Computer Science, Jiangnan University, Wuxi 214122, China.
School of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, China.
School of Information Technology and Electrical Engineering, the University of Queensland, Brisbane QLD 4072, Australia.
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Abstract

In this paper, we propose a multi-kernel multi-view canonical correlations (M 2CCs) framework for subspace learning. In the proposed framework, the input data of each original view are mapped into multiple higher dimensional feature spaces by multiple nonlinear mappings determined by different kernels. This makes M 2CC can discover multiple kinds of useful information of each original view in the feature spaces. With the framework, we further provide a specific multi-view feature learning method based on direct summation kernel strategy and its regularized version. The experimental results in visual recognition tasks demonstrate the effectiveness and robustness of the proposed method.

Keywords

image recognitioncanonical correlationmultiple kernel learningmulti-view datafeature learning

References

[1]
Horst, P. Relations among m sets of measures. Psychometrika Vol. 26, No. 2, 129-149, 1961.
Crossref Google Scholar
[2]
Kettenring, J. R. Canonical analysis of several sets of variables. Biometrika Vol. 58, No. 3, 433-451, 1971.
Crossref Google Scholar
[3]
Li, Y. O.; Adali, T.; Wang, W.; Calhoun, V. D. Joint blind source separation by multiset canonical correlation analysis. IEEE Transactions on Signal Processing Vol. 57, No. 10, 3918-3929, 2009.
Crossref Google Scholar
[4]
Correa, N. M.; Eichele, T.; Adalı, T.; Li, Y.-O.; Calhoun, V. D. Multi-set canonical correlation analysis for the fusion of concurrent single trial ERP and functional MRI. NeuroImage Vol. 50, No. 4, 1438-1445, 2010.
Crossref Google Scholar
[5]
Li, Y.-O.; Eichele, T.; Calhoun, V. D.; Adali, T. Group study of simulated driving fMRI data by multiset canonical correlation analysis. Journal of Signal Processing Systems Vol. 68, No. 1, 31-48, 2012.
Crossref Google Scholar
[6]
Nielsen, A. A. Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data. IEEE Transactions on Image Processing Vol. 11, No. 3, 293-305, 2002.
Crossref Google Scholar
[7]
Thompson, B.; Cartmill, J.; Azimi-Sadjadi, M. R.; Schock, S. G. A multichannel canonical correlation analysis feature extraction with application to buried underwater target classification. In: Proceedings of International Joint Conference on Neural Networks, 4413-4420, 2006.
Crossref
[8]
Bach, F. R.; Jordan, M. I. Kernel independent component analysis. The Journal of Machine Learning Research Vol. 3, 1-48, 2003.
Google Scholar
[9]
Yu, S.; De Moor, B.; Moreau, Y. Learning with heterogenous data sets by weighted multiple kernel canonical correlation analysis. In: Proceedings of IEEE Workshop on Machine Learning for Signal Processing, 81-86, 2007.
Crossref
[10]
Lanckriet, G. R. G.; Cristianini, N.; Bartlett, P.; Ghaoui, L. E.; Jordan, M. I. Learning the kernel matrix with semidefinite programming. The Journal of Machine Learning Research Vol. 5, 27-72, 2004.
Google Scholar
[11]
Sonnenburg, S.; Rätsch, G.; Schäfer, C.; Schölkopf, B. Large scale multiple kernel learning. The Journal of Machine Learning Research Vol. 7, 1531-1565, 2006.
Google Scholar
[12]
Rupnik, J.; Shawe-Taylor, J. Multi-view canonical correlation analysis. In: Proceedings of Conference on Data Mining and Data Warehouses, 2010. Available at http://ailab.ijs.si/dunja/SiKDD2010/Papers/Rupnik_Final.pdf.
[13]
Hardoon, D. R.; Szedmak, S. R.; Shawe-Taylor, J. R. Canonical correlation analysis: An overview with application to learning methods. Neural Computation Vol. 16, No. 12, 2639-2664, 2004.
Crossref Google Scholar
[14]
Chapelle, O.; Vapnik, V.; Bousquet, O.; Mukherjee, S. Choosing multiple parameters for support vector machines. Machine Learning Vol. 46, Nos. 1-3, 131-159, 2002.
Crossref Google Scholar
[15]
Wang, Z.; Chen, S.; Sun, T. MultiK-MHKS: A novel multiple kernel learning algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 30, No. 2, 348-353, 2008.
Crossref Google Scholar
[16]
Rakotomamonjy, A.; Bach, F.; Canu, S.; Grandvalet, Y. More efficiency in multiple kernel learning. In: Proceedings of the 24th International Conference on Machine Learning, 775-782, 2007.
Crossref
[17]
Xu, X.; Tsang, I. W.; Xu, D. Soft margin multiple kernel learning. IEEE Transactions on Neural Networks and Learning Systems Vol. 24, No. 5, 749-761, 2013.
Crossref Google Scholar
[18]
Kim, S.-J.; Magnani, A.; Boyd, S. Optimal kernel selection in kernel fisher discriminant analysis. In: Proceedings of the 23rd International Conference on Machine Learning, 465-472, 2006.
Crossref
[19]
Yan, F.; Kittler, J.; Mikolajczyk, K.; Tahir, A. Non-sparse multiple kernel fisher discriminant analysis. The Journal of Machine Learning Research Vol. 13, No. 1, 607-642, 2012.
Google Scholar
[20]
Lin, Y. Y.; Liu, T. L.; Fuh, C. S. Multiple kernel learning for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 33, No. 6, 1147-1160, 2011.
Crossref Google Scholar
[21]
Yuan, Y.-H.; Shen, X.-B.; Xiao, Z.-Y.; Yang, J.-L.; Ge, H.-W.; Sun, Q.-S. Multiview correlation feature learning with multiple kernels. In: Lecture Notes in Computer Science, Vol. 9243. He, X.; Gao, X.; Zhang, Y. et al. Eds. Springer International Publishing, 518-528, 2015.
Crossref
[22]
Kan, M.; Shan, S.; Zhang, H.; Lao, S.; Chen, X. Multi-view discriminant analysis. In: Lecture Notes in Computer Science, Vol. 7572. Fitzgibbon, A.; Lazebnik, S.; Perona, P.; Sato, Y.; Schmid, C. Eds. Springer Berlin Heidelberg, 808-821, 2012.
Crossref
[23]
Schölkopf, B.; Smola, A.; Müller, K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation Vol. 10, No. 5, 1299-1319, 1998.
Crossref Google Scholar
[24]
Chu, M. T.; Watterson, J. L. On a multivariate eigenvalue problem, part I: Algebraic theory and a power method. SIAM Journal on Scientific Computing Vol. 14, No. 5, 1089-1106, 1993.
Crossref Google Scholar
[25]
Yuan, Y.-H.; Sun, Q.-S. Fractional-order embedding multiset canonical correlations with applications to multi-feature fusion and recognition. Neurocomputing Vol. 122, 229-238, 2013.
Crossref Google Scholar
[26]
Yuan, Y.-H.; Sun, Q.-S. Graph regularized multiset canonical correlations with applications to joint feature extraction. Pattern Recognition Vol. 47, No. 12, 3907-3919, 2014.
Crossref Google Scholar
[27]
Yuan, Y.-H.; Sun, Q.-S. Multiset canonical correlations using globality preserving projections with applications to feature extraction and recognition. IEEE Transactions on Neural Networks and Learning Systems Vol. 25, No. 6, 1131-1146, 2014.
Crossref Google Scholar
[28]
Dai, D. Q.; Yuen, P. C. Face recognition by regularized discriminant analysis. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) Vol. 37, No. 4, 1080-1085, 2007.
Crossref Google Scholar
Computational Visual Media
Volume 2 Issue 2,
June 2016
Pages 153-162
DOI: 10.1007/s41095-016-0044-6
Cite this article:
Yuan Y-H, Li Y, Liu J, et al. Learning multi-kernel multi-view canonical correlations for image recognition. Computational Visual Media, 2016, 2(2): 153-162. https://doi.org/10.1007/s41095-016-0044-6

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Revised: 01 December 2015
Accepted: 08 February 2016
Published: 12 April 2016
© The Author(s) 2016

This article is published with open access at Springerlink.com

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