image recognition, canonical correlation, multiple kernel learning, multi-view data, feature learning
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 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.
Tsinghua University Press
Yun-Hao Yuan, Yun Li, Jianjun Liu et al. Learning multi-kernel multi-view canonical correlations for image recognition. Computational Visual Media 2016, 2(2): 153-162.