Multi-view multi-label learning with view feature attention allocation

In multi-view multi-label learning, instances can be described by a variety of view features, and they are also associated with a set of labels. Most of the existing multi-view multi-label learning methods extract common and private features from views. However, these methods ignore the fact that the extracted features have different importance for multi-label prediction. To address this issue, this paper proposes a multi-view multi-label learning framework with view feature attention allocation. First, the common and private features between different views are obtained. Then, the original features are compared with the common features to obtain the similarity. Next, the attention weight matrix can be obtained by multiplying the similarity with the synergistic feature matrix. Finally, the acquired attention is used to reconstruct the synergistic feature matrix that indicates the semantic information of the view for multi-label prediction. To verify the effectiveness of the proposed algorithm, experiments are conducted on seven multi-view multi-label datasets, and five advanced algorithms are taken for performance comparison. The extensive experimental results demonstrate the superiority of our algorithm.