32 citations · 41 across the 5 of their papers we have counts for
7 papers
Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence
Xihong Yang, Siwei Wang, Jiaqi Jin +6
Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views. Many existing MVC methods heavily rely on the assumption of view consistency, whe…
Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios
Xihong Yang, Siwei Wang, Fangdi Wang +6
Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source info…
Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning
Yuzhuo Dai, Jiaqi Jin, Zhibin Dong +6
In incomplete multi-view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsistencies across views. A feasible solution is to explore cross-vie…
Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance
Jiaqi Jin, Siwei Wang, Zhibin Dong +4
Multi-view clustering leverages complementary representations from diverse sources to enhance performance. However, real-world data often suffer incomplete cases due to factors lik…
DealMVC: Dual Contrastive Calibration for Multi-view Clustering
Xihong Yang, Jiaqi Jin, Siwei Wang +7
Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe the…
Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment
Jiaqi Jin, Siwei Wang, Zhibin Dong +2
The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partia…
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences
Siwei Wang, Xinwang Liu, Suyuan Liu +4
Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied…