AEMVC: Mitigate Imbalanced Embedding Space in Multi-view Clustering

Pengyuan Li, Man Liu, Dongxia Chang*, Yiming Wang, Zisen Kong, Yao Zhao

Paper | Code

• We found that the embedding space learned using the encoder-decoder architecture cannot embrace the efficacy of different feature directions. Therefore, we propose a novel Activate-Then-Eliminate Strategy for Multi-View Clustering to adjust the contribution strength of different feature directions dynamically.

Deep Multi-view Clustering with Intra-view Similarity and Cross-view Correlation Learning

Pengyuan Li, Dongxia Chang*, Yiming Wang, Man Liu, Zisen Kong, Linhua Kong, Yao Zhao

Paper | Code

• We present a novel deep learning framework that mitigates view bias through joint optimization of intra-view similarity and cross-view correlation. The proposed method enhances fine-grained structures within each view and adaptively balances diverse information across views, ultimately improving clustering performance.

DCMVC: Dual Contrastive Multi-view Clustering

Pengyuan Li, Dongxia Chang*, Zisen Kong, Yiming Wang, Yao Zhao

Paper | Code

• We propose a novel deep contrastive multi-view clustering method termed DCMVC. The dual contrastive mechanism can alleviate the constraints of a single positive sample on contrastive learning by incorporating category information to regularize the feature structure and fully explore the consistency of similar samples.

Bipartite Contrastive Multi-view Clustering with Singular Value Modulation

Teng Zhang, Pengyuan Li, Zisen Kong, Dongxia Chang∗, Yao Zhao

Paper | Code

• We reformulate contrastive learning as a binary classification problem, avoiding the limitation in previous contrastive methods that heavily depend on naturally paired data. By capturing sample-level and category-level consistency relationships among multiple views, the learned representations are further refined.