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Found 3 papers, 1 papers with code
Low-Rank Tensor Based Proximity Learning for Multi-View Clustering
To deal with these problems, we propose a novel Low-rank Tensor Based Proximity Learning (LTBPL) approach for multi-view clustering, where multiple low-rank probability affinity matrices and consensus indicator graph reflecting the final performances are jointly studied in a unified framework.
Dual Information Enhanced Multi-view Attributed Graph Clustering
Specifically, the proposed method introduces the Specific Information Reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables GCN to capture the more essential low-level representations.
Adaptively-weighted Integral Space for Fast Multiview Clustering
In view of this, we propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity.
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