Discriminative Feature Selection Guided Deep Canonical Correlation Analysis

Nour El-Din El-Madany, Yifeng He, Ling Guan

Abstract: This paper proposes a novel Discriminative Feature Selection Guided Deep Canonical Correlation Analysis (D 2 CCA) for multiview learning. The proposed (D 2 CCA) enhances the discriminative power of the learned featured representation by imposing the selection of the most discriminative features. Moreover, it learns to maximize the correlations between two views. Also, an alternating iterative learning algorithm is presented to find the sub-optimal solution. The experimental results demonstrated that the proposed (D 2 CCA) can achieve a higher average accuracy compared to several existing methods.