CG-MVC: structure-aware multi-view clustering with entropy-quantile curriculum learning

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.

SCD-MVC: Stable Conditional Diffusion for Multi-view Clustering

Multi-view clustering has garnered significant attention for its ability to integrate heterogeneous data and uncover underlying categorical structures. However, prevailing autoencoder-based methods often yield indiscriminative embeddings, rendering them prone to trivial solutions. While diffusion models have shown prom...

Self-Refining Spherical Consensus Embedding for Constrained Multi-View Clustering.

Constrained multi-view clustering aims to integrate external prior knowledge and complementary information from multiple views to enhance clustering performance. However, existing approaches typically employ Euclidean distance to learn view-specific and consensus embeddings, which often fail to capture the intrinsic ge...

Self-supervised probability distribution-driven deep incomplete multi-view clustering

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...

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.