Research map / Learning theory and algorithms / Clustering
Consistent and Specific Multi-View Subspace Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| CSMSC vs best baseline (DiMSC) | multi-view subspace clustering · Notting-Hill | NMI: 0.799 -> 0.908 | Helped |
| CSMSC vs DiMSC | multi-view subspace clustering · BBCSport | NMI: 0.885 -> 0.881 | No clear effect |
| CSMSC vs LT-MSC | multi-view subspace clustering · Yale | NMI: 0.765 -> 0.784 | Helped |
| add view-specific representations (ablation) | CMSC · Notting-Hill | NMI: 0.209 -> 0.908 | Helped |
| add view-specific representations (ablation) | CMSC · Yale | NMI: 0.628 -> 0.784 | Helped |
| add shared consistent representation (ablation) | SMSC · Yale | NMI: 0.677 -> 0.784 | Helped |
| add shared consistent representation (ablation) | SMSC · ORL | NMI: 0.865 -> 0.942 | Helped |
| use three views instead of single intensity view (ablation) | CSMSC · Yale | NMI: 0.684 -> 0.775 | Helped |
| lambda_C or lambda_D near zero (ablation) | CSMSC · Yale | NMI | Hurt |
Closest papers by meaning
- Scalable One-Pass Incomplete Multi-View Clustering by Aligning Anchors (AAAI 2025 · similarity 0.90)
- Partial Multi-View Clustering via Self-Supervised Network (AAAI 2024 · similarity 0.88)
- Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding (AAAI 2024 · similarity 0.88)
- From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement (NeurIPS 2024 · similarity 0.88)
- Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion (NeurIPS 2025 · similarity 0.88)
- Robust Consensus Anchor Learning for Efficient Multi-view Subspace Clustering (ICML 2025 · similarity 0.88)
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