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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CCA) for multiview learning. The proposed (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CCA) can achieve a higher average accuracy compared to several existing methods.
BibTeX
@inproceedings{icassp2019_discriminativefe,
title = {Discriminative Feature Selection Guided Deep Canonical Correlation Analysis},
author = {Nour El-Din El-Madany and Yifeng He and Ling Guan},
booktitle = {ICASSP 2019},
year = {2019}
}