Incomplete multi-view clustering
16 papers with code • 1 benchmarks • 0 datasets
Most implemented papers
Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering
2023-MindSpore-1/ms-code-115
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ACM International Conference on Multimedia 2022
To address these issues, we propose a Robust Diversified Graph Contrastive Network (RDGC) for incomplete multi-view clustering, which integrates multi-view representation learning and diversified graph contrastive regularization into a unified framework.
Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View Clustering
ckghostwj/CVPR2023_code
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CVPR 2023
Graph-based multi-view clustering has attracted extensive attention because of the powerful clustering-structure representation ability and noise robustness.
Incomplete Multi-view Clustering via Prototype-based Imputation
XLearning-SCU/2023-IJCAI-ProImp
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Thanks to our dual-stream model, both cluster- and view-specific information could be captured, and thus the instance commonality and view versatility could be preserved to facilitate IMvC.
Scalable Incomplete Multi-View Clustering with Structure Alignment
Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID).
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering
We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition based graph filter for robust clustering. JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor.
Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding
In this work, we proposed a novel Incomplete Contrastive Multi-View Clustering method with high-confidence guiding (ICMVC).
