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Conference
Open access
Sep 2026
Salient-Residual Decoupled Multi-View Learning for Clustering
This paper proposes salient-residual decoupled multi-view learning for clustering, SRDMVC, introducing a novel decomposition-fusion iterative optimization, which separates the feature space into a salient space and a residual subspace effectively and fuses them using a novel attention mechanism.
Gao-Kai Wang, Yazhou Ren, Feng-Yu Zhang et al.
· Proceedings of the Thirty-Fi... · 0 citations
Conference
Open access
Sep 2026
SCD-MVC: Stable Conditional Diffusion for Multi-view Clustering
SCD-MVC is proposed, a Stable Conditional Diffusion-driven framework for Multi-View Clustering that integrates conditional diffusion generation into the latent encoder to generate a target view conditioned on the remaining views, thereby facilitating robust joint distribution modeling.
Jinlin Ma, Chen-Kai Guo, Ren-Da Han et al.
· Proceedings of the Thirty-Fi... · 0 citations
Aug 2026
Self-Refining Spherical Consensus Embedding for Constrained Multi-View Clustering.
This work proposes a novel deep constrained multi-view clustering framework, namely SeSCE, which encodes view embeddings into a spherical space, leveraging pairwise constraints to maximize intra-class compactness and inter-class separability and introduces a confidence-aware pseudo-constraint mining mechanism.
Jun Wang, Zhenglai Li, Chuan Tang et al.
· IEEE Transactions on Pattern... · 0 citations
Aug 2026
Self-supervised probability distribution-driven deep incomplete multi-view clustering
This work proposes a Self-supervised Probability Distribution-driven Deep Incomplete Multi-view Clustering method (SPDIM), which first handles missing views at the data level, utilizing uncertainty-aware sampling to interpolate missing information and thereby enforce global consistency across samples.
Shengxia Gao, Wei-Yi Wei, Yongxin Shi et al.
· Multimedia Systems · 0 citations
Sep 2026
Adaptive Multi-Stage Feature-View Fusion via Deep Graph Representation for Clustering
Deep neural networks primarily aim to enhance the representation ability of high-level semantic features by deepening the network to reinforce critical features. However, this often leads to the under-utilization or discarding of certain low-level information, resulting in suboptimal performance. Due to differences in...
Rui Zhang, Yue-Long Cheng, Jing-Fan Yang et al.
· IEEE Transactions on Image P... · 0 citations
Conference
Open access
Sep 2026
Dual-Topology Learning with Adaptive Anchors for Multi-View Clustering
The Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold.
Cheng-Long Zhang, Chao Zhang, Jun-Hao Zhang et al.
· Proceedings of the Thirty-Fi... · 0 citations