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Abstract
canonical correlations (M2CCs) 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 M2CC 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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Details
1 Yangzhou University, Department of Computer Science, College of Information Engineering, Yangzhou, China (GRID:grid.268415.c); Jiangnan University, Department of Computer Science, Wuxi, China (GRID:grid.258151.a) (ISNI:0000000107081323)
2 Yangzhou University, Department of Computer Science, College of Information Engineering, Yangzhou, China (GRID:grid.268415.c)
3 Jiangnan University, Department of Computer Science, Wuxi, China (GRID:grid.258151.a) (ISNI:0000000107081323)
4 Nanjing University of Science and Technology, School of Computer Science, Nanjing, China (GRID:grid.410579.e) (ISNI:0000000091169901); the University of Queensland, School of Information Technology and Electrical Engineering, Brisbane QLD, Australia (GRID:grid.1003.2) (ISNI:0000000093207537)
5 Nanjing University of Science and Technology, School of Computer Science, Nanjing, China (GRID:grid.410579.e) (ISNI:0000000091169901)