In this paper, we propose a multi-kernel multi-view canonical correlations(M2CCs) framework for subspace learning. In the proposed framework,the input data of each original view are mapped into multiple higher dimensi...In this paper, we propose a multi-kernel multi-view 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 M2 CC 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.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos. 61402203, 61273251, and 61170120the Fundamental Research Funds for the Central Universities under Grant No. JUSRP11458the Program for New Century Excellent Talents in University under Grant No. NCET-12-0881
文摘In this paper, we propose a multi-kernel multi-view 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 M2 CC 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.
