Discriminative Scatter Regularized CCA for Multiview Image Feature Learning and Recognition

In this paper, we propose a novel supervised canonical correlation analysis approach based on discriminative scatter regularization for multiview image feature learning. This method at the same time considers the between-view correlations and within-view class label information of training samples. The proposed method is applied to handwritten digit image recognition. The experimental results on multiple feature dataset demonstrate the superior performance of our approach compared with the existing multiview feature learning methods.

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