Multi-level Feature Learning for Contrastive Multi-view Clustering

Understand

Multi-view clustering can explore common semantics from multiple views and has attracted increasing attention.

• However, existing works punish multiple objectives in the same feature space, where they ignore the conflict between learning consistent common semantics and reconstructing inconsistent view-private information.
• In this paper, we propose a new framework of multi-level feature learning for contrastive multi-view clustering to address the aforementioned issue.
• Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces.