View-Channel Mixer Network for Double Incomplete Multi-View Multi-Label learning

In reality, multi-view multi-label learning has a richer descriptive power than single-view single-label learning. However, due to multiple uncertainties in practical situations, the process of collecting data often results in missing views and labels. Therefore, the double incomplete multi-view multi-label classification task emerged. In this complex field, two issues can be characterized as follows: i) How to embed complementary learning objectives in the view feature space ? ii) How to jointly learn the consistency and complementarity of views? To this end, this paper proposes a View-Channel Mixer Network (VCMN) based on the mixing operations of a multilayer perceptron to address this complex problem. First, we designed a View-Channel Mixer module to model the complementary relationships in the high-dimensional feature space of the views. Second, cross-view and cross-sample contrastive loss is used to enhance the network’s ability to learn consistent representations across multiple views, enabling the joint learning of complementarity and consistency. Our method demonstrates competitive experimental results compared to other state-of-the-art methods on five widely used multi-view multi-label datasets.