Multi-view Classification via Adaptive Discriminant Analysis

Deyan Xie, Qin Li, Wei Xia, Shiwei Pang, Huihui He, Quanxue Gao
2019 IEEE Access  
In many real applications, an object is usually represented with multiple views, providing compatible and complementary information to each other. Therefore, it is highly desirable to recognize the object from distinct and even heterogeneous views. In this paper, we propose a novel method, the named multi-view locality adaptively discriminant analysis (MvLADA), for multi-view classification. The MvLADA integrates subspace learning and weighted matrix learning into a uniform framework, where the
more » ... weighted matrix is adaptively attained and shared by all views. Compared with the most existing LDA-based multi-view methods, the MvLADA adaptively assigns different weights to each sample, which enhances MvLADA's flexibility in practical applications. Moreover, the learned weighted matrix shared by all views exploits the point's neighbor relationship automatically without requiring a kNN procedure. Besides, the MvLADA is a parameter-free method without imposing any additional parameters. We validate the proposed MvLADA on three real-world datasets, indicating a better performance than the state-of-the-art multi-view algorithms. INDEX TERMS Multi-view classification, local geometric structure, discriminant analysis. 36702 2169-3536 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. VOLUME 7, 2019 HUIHUI HE is currently pursuing the M.S. degree in communication and information system with Xidian University, Xi'an, China. Her research interests include machine learning and multi-view learning. QUANXUE GAO received the B.Eng. degree from Xi'an Highway University,
doi:10.1109/access.2019.2905008 fatcat:u6zpgxrx4nbnhf43keinbv5r3u