Label-Specific Alignment with Adversarial Multi-View Representation

In Multi-View Multi-Label (MVML) learning, each instance is represented with several feature vectors and associated with multiple labels simultaneously. Existing models are mainly built on a shared latent space among multiple views and tend to select features that are distinguishable for all labels. However, they ignore the distinctive characteristic of each view and fail to extract the discrimina...