Multi-View Classification via Adaptive Discriminant Analysis

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 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 k NN 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.