Enhanced feature fusion through irrelevant redundancy elimination in intra-class and extra-class discriminative correlation analysis

• Main Authors: Wu, Zuobin, Mao, Kezhi, Ng, Gee-Wah
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Article
• 語言: English
• 出版: 2020
• 主題: Engineering::Electrical and electronic engineering; Feature Fusion; Canonical Correlation Analysis
• 在線閱讀: https://hdl.handle.net/10356/144648
• 機構: Nanyang Technological University

Feature fusion aims to provide enhancements of data authenticity in both traditional and deep learning pattern analysis. Canonical Correlation Analysis (CCA) based feature fusion is a main technique for exploring the mutual relationships of multiple feature sets. In traditional CCA-based feature fus...