Multi-view Multi-label Learning with Shared Features Inconsistency

Abstract

AbstractMulti-view multi-label (MVML) learning is a framework for solving the problem of associating a single instance with a set of class labels in the presence of multiple types of data features. The extraction of shared features among multiple views for label prediction is a common MVML learning method. However, previous approaches assumed that the number and association degree of shared features were the same across views. In fact, they differ in the number and degree of association. The above assumption can lead to a poor communicability of the views. Therefore, this paper proposes an MVML learning method based on the inconsistent shared features extracted by the graph attention model. The first step is to extract the shared and private features of multiple views. Next, the graph attention mechanism is adopted to learn the association degree of shared features of different views and calculate the adjacency matrix and attention coefficient. The number of associations is determined by taking the obtained adjacency matrix as a mask matrix, while the association degree of shared features is measured by the attention weight matrix. Finally, the new shared features are obtained for multi-label prediction. We conducted experiments on seven MVML datasets to compare the proposed algorithm with seven advanced algorithms. The experimental results demonstrate the advantages of our algorithm.