Research map / Learning theory and algorithms / Clustering
Multi-View Multiple Clusterings Using Deep Matrix Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DMClusts instead of OSC | Multi-view clustering · Caltech7 | SC: 0.266$\pm$0.000 -> 0.301$\pm$0.006 | Helped |
| Use DMClusts instead of MVMC | Multi-view clustering · BBCSport | SC: 0.269$\pm$0.000 -> 0.284$\pm$0.006 | Helped |
| Use DMClusts instead of OSC | Multi-view clustering · Handwritten | SC: 0.352$\pm$0.000 -> 0.377$\pm$0.012 | Helped |
| Use DMClusts instead of OSC | Multi-view clustering · MSRCv1 | SC: 0.382$\pm$0.011 -> 0.556$\pm$0.012 | Helped |
| Use DMClusts instead of OSC | Multi-view clustering · Yale | SC: 0.221$\pm$0.005 -> 0.303$\pm$0.019 | Helped |
| Use DMClusts instead of DMF | Multi-view clustering · Reuters | SC: 0.314$\pm$0.004 -> 0.344$\pm$0.006 | Helped |
| Use DMClusts instead of DMF | Multi-view clustering · Mirflickr | SC: 0.005$\pm$0.000 -> 0.336$\pm$0.008 | Helped |
| Weight views separately for each clustering, including zero weight for an injected noisy view | DMClusts · Reuters with injected Gaussian noisy view | robustness to noisy views: NMI: 0.019, JC: 0.161 | Helped |
| Add view weighting and redundancy control rather than using DMF on concatenated features | DMClusts versus DMF · Reuters with injected Gaussian noisy view | SC: DMF SC: 0.158 | Helped |
| Increase diversity-control weight λ (ablation) | DMClusts · Yale | DI | Hurt |
| Increase diversity-control weight λ (ablation) | DMClusts · Yale | 1-NMI | Helped |
| Vary view-weight distribution parameter r (ablation) | DMClusts · Yale | 1-NMI | Mixed |
Closest papers by meaning
- Medusa: A Multi-Scale High-order Contrastive Dual-Diffusion Approach for Multi-View Clustering (CVPR 2025 · similarity 0.91)
- EASEMVC:Efficient Dual Selection Mechanism for Deep Multi-View Clustering (CVPR 2025 · similarity 0.89)
- Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures (AAAI 2024 · similarity 0.88)
- Fast Incomplete Multi-view Clustering by Flexible Anchor Learning (ICML 2025 · similarity 0.88)
- Deep Fair Multi-View Clustering with Attention KAN (CVPR 2025 · similarity 0.88)
- A Non-parametric Graph Clustering Framework for Multi-View Data (AAAI 2024 · similarity 0.88)
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