Research map / Learning theory and algorithms / Clustering
Multi-View Clustering via Deep Matrix Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| deep multi-layer semi-NMF instead of single-layer multi-view representation | multi-view clustering · Yale | NMI: 0.727 -> 0.782 | Helped |
| deep multi-layer semi-NMF | multi-view clustering · Extended YaleB | ACC: 0.615 -> 0.763 | Helped |
| deep multi-layer semi-NMF | multi-view clustering · Notting-Hill | NMI: 0.799 -> 0.797 | No clear effect |
| deep multi-layer semi-NMF | multi-view clustering · Notting-Hill | ACC: 0.843 -> 0.871 | Helped |
| vary layer size and gamma (ablation) | proposed method · Yale | NMI | Mixed |
Closest papers by meaning
- Medusa: A Multi-Scale High-order Contrastive Dual-Diffusion Approach for Multi-View Clustering (CVPR 2025 · similarity 0.90)
- A Non-parametric Graph Clustering Framework for Multi-View Data (AAAI 2024 · similarity 0.89)
- Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures (AAAI 2024 · similarity 0.89)
- Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios (CVPR 2024 · similarity 0.89)
- EASEMVC:Efficient Dual Selection Mechanism for Deep Multi-View Clustering (CVPR 2025 · similarity 0.88)
- SURER: Structure-Adaptive Unified Graph Neural Network for Multi-View Clustering (AAAI 2024 · similarity 0.88)
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