Research map / Learning theory and algorithms / Clustering
Discriminatively Embedded K-Means for Multi-View Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DEKM's jointly learned discriminative subspaces and shared clustering instead of RMVKM | Multi-view clustering · MSRCv1 | ACC: 0.9048 -> 0.9238 | Helped |
| Use DEKM's jointly learned discriminative subspaces and shared clustering instead of RMVKM | Multi-view clustering · MSRCv1 | NMI: 0.8463 -> 0.8649 | Helped |
| Use DEKM's jointly learned discriminative subspaces and shared clustering instead of SDEKM | Multi-view clustering · Caltech101-7 | ACC: 0.8254 -> 0.8503 | Helped |
| Use DEKM's jointly learned discriminative subspaces and shared clustering instead of SDEKM | Multi-view clustering · Caltech101-7 | NMI: 0.7465 -> 0.8231 | Helped |
| Use DEKM's jointly learned discriminative subspaces and shared clustering instead of SDEKM | Multi-view clustering · Handwritten | ACC: 0.9355 -> 0.9530 | Helped |
| Use DEKM's jointly learned discriminative subspaces and shared clustering instead of SDEKM | Multi-view clustering · Handwritten | NMI: 0.8753 -> 0.9098 | Helped |
| Add adaptive view-weight learning (full DEKM rather than SDEKM) (ablation) | DEKM · MSRCv1 | ACC: 0.8810 -> 0.9238 | Helped |
| Add adaptive view-weight learning (full DEKM rather than SDEKM) (ablation) | DEKM · MSRCv1 | NMI: 0.8002 -> 0.8649 | Helped |
| Add adaptive view-weight learning (full DEKM rather than SDEKM) (ablation) | DEKM · Caltech101-7 | ACC: 0.8254 -> 0.8503 | Helped |
| Add adaptive view-weight learning (full DEKM rather than SDEKM) (ablation) | DEKM · Caltech101-7 | NMI: 0.7465 -> 0.8231 | Helped |
| Add adaptive view-weight learning (full DEKM rather than SDEKM) (ablation) | DEKM · Handwritten | ACC: 0.9355 -> 0.9530 | Helped |
| Add adaptive view-weight learning (full DEKM rather than SDEKM) (ablation) | DEKM · Handwritten | NMI: 0.8753 -> 0.9098 | Helped |
Closest papers by meaning
- Discriminatively Fuzzy Multi-View K-means Clustering with Local Structure Preserving (AAAI 2024 · similarity 0.88)
- Partial Multi-View Clustering via Self-Supervised Network (AAAI 2024 · similarity 0.87)
- Sharper Error Bounds in Late Fusion Multi-view Clustering with Eigenvalue Proportion Optimization (AAAI 2025 · similarity 0.87)
- Scalable One-Pass Incomplete Multi-View Clustering by Aligning Anchors (AAAI 2025 · similarity 0.87)
- Towards Resource-friendly, Extensible and Stable Incomplete Multi-view Clustering (ICML 2024 · similarity 0.86)
- Fast Incomplete Multi-view Clustering by Flexible Anchor Learning (ICML 2025 · similarity 0.86)
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