  Multi-view learning has become a popular research topic in recent years, but
research on the cross-application of classic multi-label classification and
multi-view learning is still in its early stages. In this paper, we focus on
the complex yet highly realistic task of incomplete multi-view weak multi-label
learning and propose a masked two-channel decoupling framework based on deep
neural networks to solve this problem. The core innovation of our method lies
in decoupling the single-channel view-level representation, which is common in
deep multi-view learning methods, into a shared representation and a
view-proprietary representation. We also design a cross-channel contrastive
loss to enhance the semantic property of the two channels. Additionally, we
exploit supervised information to design a label-guided graph regularization
loss, helping the extracted embedding features preserve the geometric structure
among samples. Inspired by the success of masking mechanisms in image and text
analysis, we develop a random fragment masking strategy for vector features to
improve the learning ability of encoders. Finally, it is important to emphasize
that our model is fully adaptable to arbitrary view and label absences while
also performing well on the ideal full data. We have conducted sufficient and
convincing experiments to confirm the effectiveness and advancement of our
model.
