The Application of Target Embedding AutoEncoder based on Knowledge Distillation in Multi-View Multi-Label Classification

In multi-view learning, how to effectively integrate features among multiple views becomes a key challenge. For multi-label tasks, it is extremely important that the relationship between features and labels. This paper proposes a Target Embedding Autoencoder framework based on knowledge distillation (tea-mvml), which explores the correlation between features and labels in Multi-View Multi-Label (MVML) learning. The framework transfers knowledge from a teacher model based on a Target Embedding Autoencoder (TEA) to a small student model through knowledge distillation. The teacher model of tea-mvml learns the relationship between features and labels in the potential space, while the student model has the generalization ability of the teacher model. Experimental results on multiple real-world datasets show that tea-mvml not only reduces the complexity of the model, but also outperforms other state-of-the-art multi-view multi-label classification approaches.