Self-Supervised Semantic Soft Label Learning Network for Deep Multi-View Clustering

Multi-view clustering, which identifies shared semantics from different perspectives and classifies data samples into distinct categories using unsupervised methods, is gaining increasing interest. This task primarily focuses on learning consistent multi-view feature representations and clustering labels. Current approaches for achieving consistent multi-view feature representations often use techniques such as cascading, weight fusion, and attention mechanism fusion. These methods reconstruct features based on original low-level features via encoder-decoder, which often contain visual private information, leading to misleading feature representations. Furthermore, in the clustering label learning process, many methods use a two-stage approach: first, they achieve consistent feature representations, and then they apply hard labeling methods like K-means or spectral clustering to obtain clustering labels. Single-stage methods typically derive consistent labels through a linear coding layer based on consistent representation learning. These methods do not fully utilize the multi-view view semantic information, and consistent representation learning may be impaired when some low-quality views are present, leading to the generation of inaccurate semantic labels. To address these issues, we propose a Self-supervised Semantic Soft Label Learning Network for Deep Multi-view Clustering. Specifically, we introduce a consensus high-level feature learning module that uses a shared MLP layer to transform low-level features into a high-level feature space. To enhance the consistency between high-level features from different views, we maximize mutual information between these features and introduce the U-Projection module, which improves the expressive power of the consensus feature via resampling the features and concatenating the fused features before and after sampling operations. Additionally, we propose a self-supervised semantic label learning module that employs a dual-branch approach to independently learn consistent view-specific semantic labels through contrastive learning, while deriving view-consensus semantic labels from shared high-level features extracted from multiple views. Finally, KL divergence is used to align the view-consensus labels with the view-specific labels. A series of extensive experiments have shown that our approach yields superior clustering results compared to existing techniques.