Research map / Learning theory and algorithms / Clustering
GCFAgg: Global and Cross-View Feature Aggregation for Multi-View Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use global cross-sample and cross-view feature aggregation instead of concatenated view-specific representations (ablation) | GCFAggMVC · Prokaryotic | ACC: 0.4403 -> 0.6225 | Helped |
| Use global cross-sample and cross-view feature aggregation instead of concatenated view-specific representations (ablation) | GCFAggMVC · CCV | ACC: 0.2850 -> 0.3543 | Helped |
| Use global cross-sample and cross-view feature aggregation instead of concatenated view-specific representations (ablation) | GCFAggMVC · MNIST-USPS | ACC: 0.9753 -> 0.9956 | Helped |
| Add the SgCL loss (ablation) | GCFAggMVC · Prokaryotic | ACC: 0.4804 -> 0.6225 | Helped |
| Add the SgCL loss (ablation) | GCFAggMVC · CCV | ACC: 0.2020 -> 0.3543 | Helped |
| Add the SgCL loss (ablation) | GCFAggMVC · MNIST-USPS | ACC: 0.6949 -> 0.9956 | Helped |
| Add structure matrix S to standard contrastive learning (ablation) | Contrastive learning · CCV | ACC: 0.2711 -> 0.3046 | Helped |
| Replace standard contrastive learning with SgCL using structure-guided pairs (ablation) | Contrastive learning · CCV | ACC: 0.2711 -> 0.3543 | Helped |
| Replace standard contrastive learning with SgCL using structure-guided pairs (ablation) | Contrastive learning · MNIST-USPS | ACC: 0.9562 -> 0.9956 | Helped |
| Add structure matrix S to standard contrastive learning (ablation) | Contrastive learning · MNIST-USPS | ACC: 0.9562 -> 0.9768 | Helped |
| Add GCFAggMVC rather than the strongest reported baseline | Multi-view clustering · Cifar100 | ACC: 0.8482 -> 0.9597 | Helped |
| Add GCFAggMVC rather than the strongest reported baseline | Multi-view clustering · MNIST-USPS | ACC: 0.9954 -> 0.9956 | Helped |
Closest papers by meaning
- Incomplete and Unpaired Multi-View Graph Clustering with Cross-View Feature Fusion (AAAI 2025 · similarity 0.90)
- Sample-Level Cross-View Similarity Learning for Incomplete Multi-View Clustering (AAAI 2024 · similarity 0.90)
- Medusa: A Multi-Scale High-order Contrastive Dual-Diffusion Approach for Multi-View Clustering (CVPR 2025 · similarity 0.89)
- Deep Fair Multi-View Clustering with Attention KAN (CVPR 2025 · similarity 0.89)
- Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding (AAAI 2024 · similarity 0.89)
- EASEMVC:Efficient Dual Selection Mechanism for Deep Multi-View Clustering (CVPR 2025 · similarity 0.88)
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