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Computational Visual Media 2016, 2(2): 153-162 https://doi.org/10.1007/s41095-016-0044-6
Research Article | Open Access | Issue | Published: 12 April 2016

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

Show Author's Information Yun-Hao Yuan1,2( ),  Yun Li1( ),  Jianjun Liu2,  Chao-Feng Li2,  Xiao-Bo Shen3,4,  Guoqing Zhang3,  Quan-Sen Sun3
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.
Keywords:
feature learning, image recognition, canonical correlation, multiple kernel learning, multi-view data
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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Abstract Full text About this article

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.


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About this article

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

Show Author's information Yun-Hao Yuan1,2( ), Yun Li1( ), Jianjun Liu2, Chao-Feng Li2, Xiao-Bo Shen3,4, Guoqing Zhang3, Quan-Sen Sun3
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.

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: feature learning, image recognition, canonical correlation, multiple kernel learning, multi-view data

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Publication history

Revised: 01 December 2015
Accepted: 08 February 2016
Published: 12 April 2016
Issue date: June 2016

Copyright

© The Author(s) 2016

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61402203, 61273251, and 61170120, the Fundamental Research Funds for the Central Universities under Grant No. JUSRP11458, and the Program for New Century Excellent Talents in University under Grant No. NCET-12-0881.

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