Research map / Learning theory and algorithms / Clustering
Multi-Level Feature Learning for Contrastive Multi-View Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MFLVC instead of the strongest reported baseline | Multi-view clustering · MNIST-USPS | ACC: 0.987 -> 0.995 | Helped |
| Use MFLVC instead of the strongest reported baseline | Multi-view clustering · BDGP | ACC: 0.981 -> 0.989 | Helped |
| Use MFLVC instead of the strongest reported baseline | Multi-view clustering · CCV | ACC: 0.296 -> 0.312 | Helped |
| Use MFLVC instead of the strongest reported baseline | Multi-view clustering · Fashion | ACC: 0.857 -> 0.992 | Helped |
| Use MFLVC instead of the strongest reported baseline | Multi-view clustering · Caltech-2V | ACC: 0.525 -> 0.606 | Helped |
| Use MFLVC instead of the strongest reported baseline | Multi-view clustering · Caltech-3V | ACC: 0.569 -> 0.631 | Helped |
| Use MFLVC instead of the strongest reported baseline | Multi-view clustering · Caltech-4V | ACC: 0.687 -> 0.733 | Helped |
| Use MFLVC instead of the strongest reported baseline | Multi-view clustering · Caltech-5V | ACC: 0.760 -> 0.804 | Helped |
| Add reconstruction loss L_Z to clustering loss L_Q (ablation) | MFLVC · MNIST-USPS | ACC: 0.676 -> 0.891 | Helped |
| Add high-level feature and semantic-label losses L_H and L_P to L_Q (ablation) | MFLVC · MNIST-USPS | ACC: 0.676 -> 0.984 | Helped |
| Add reconstruction loss L_Z to clustering loss L_Q (ablation) | MFLVC · BDGP | ACC: 0.715 -> 0.825 | Helped |
| Add high-level feature and semantic-label losses L_H and L_P to L_Q (ablation) | MFLVC · BDGP | ACC: 0.715 -> 0.955 | Helped |
Closest papers by meaning
- Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding (AAAI 2024 · similarity 0.91)
- Self-supervised Trusted Contrastive Multi-view Clustering with Uncertainty Refined (AAAI 2025 · similarity 0.90)
- EASEMVC:Efficient Dual Selection Mechanism for Deep Multi-View Clustering (CVPR 2025 · similarity 0.89)
- Ambiguous Instance-Aware Contrastive Network with Multi-Level Matching for Multi-View Document Clustering (AAAI 2025 · similarity 0.89)
- Incomplete and Unpaired Multi-View Graph Clustering with Cross-View Feature Fusion (AAAI 2025 · similarity 0.88)
- Partial Multi-View Clustering via Self-Supervised Network (AAAI 2024 · similarity 0.88)
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