Deep Contrastive Multi-view Clustering under Semantic Feature Guidance
Siwen Liu Jinyan Liu Hanning Yuan Qi Li Jing Geng Ziqiang Yuan Huaxu Han
Contrastive learning has achieved promising performance in the field of
multi-view clustering recently. However, the positive and negative sample
construction mechanisms ignoring semantic consistency lead to false negative
pairs, limiting the performance of existing algorithms from further
improvement. To solve this problem, we propose a multi-view clustering
framework named Deep Contrastive Multi-view Clustering under Semantic feature
guidance (DCMCS) to alleviate the influence of false negative pairs.
Specifically, view-specific features are firstly extracted from raw features
and fused to obtain fusion view features according to view importance. To
mitigate the interference of view-private information, specific view and fusion
view semantic features are learned by cluster-level contrastive learning and
concatenated to measure the semantic similarity of instances. By minimizing
instance-level contrastive loss weighted by semantic similarity, DCMCS
adaptively weakens contrastive leaning between false negative pairs.
Experimental results on several public datasets demonstrate the proposed
framework outperforms the state-of-the-art methods.