Research map / Graph learning / Graph algorithms and clustering
Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| MLAN (adaptive-neighbour graph learning) vs MMSC | multi-view clustering · MSRC-v1 | clustering accuracy: 0.688 -> 0.738 | Helped |
| MLAN vs Co-reg | multi-view clustering · MSRC-v1 | clustering accuracy: 0.724 -> 0.738 | Helped |
| MLAN vs AMMSS | semi-supervised classification · NUS-WIDE | accuracy: 0.237 -> 0.238 | No clear effect |
| MLAN vs AMMSS | semi-supervised classification · MSRC-v1 | accuracy: 0.821 -> 0.863 | Helped |
| use multiple views instead of best single view | spectral clustering · HW | clustering accuracy | Helped |
Closest papers by meaning
- SURER: Structure-Adaptive Unified Graph Neural Network for Multi-View Clustering (AAAI 2024 · similarity 0.90)
- A Non-parametric Graph Clustering Framework for Multi-View Data (AAAI 2024 · similarity 0.90)
- Medusa: A Multi-Scale High-order Contrastive Dual-Diffusion Approach for Multi-View Clustering (CVPR 2025 · similarity 0.88)
- Fast Incomplete Multi-view Clustering by Flexible Anchor Learning (ICML 2025 · similarity 0.88)
- EASEMVC:Efficient Dual Selection Mechanism for Deep Multi-View Clustering (CVPR 2025 · similarity 0.88)
- Structure-Adaptive Multi-View Graph Clustering for Remote Sensing Data (AAAI 2025 · similarity 0.88)
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