Research map / Learning theory and algorithms / Clustering
Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| anchor-based fused similarity + spectral clustering (APMC) vs MIC | partial multi-view clustering · 3Sources BBC-Reuters | ACC: 42.97±2.74 -> 75.27±1.42 | Helped |
| APMC vs MIC | partial multi-view clustering · 3Sources (all views) | ACC: 74.64±6.08 -> 80.31±1.47 | Helped |
| replace averaged per-view similarity (SC[A]) with anchor-based similarity reconstruction | spectral clustering · 3Sources (all views) | ACC: 44.78±0.38 -> 80.31±1.47 | Helped |
| vary number of nearest anchors m (2-14) (ablation) | APMC · Digit | ACC | No clear effect |
Closest papers by meaning
- Learn from View Correlation: An Anchor Enhancement Strategy for Multi-view Clustering (CVPR 2024 · similarity 0.93)
- DVSAI: Diverse View-Shared Anchors Based Incomplete Multi-View Clustering (AAAI 2024 · similarity 0.91)
- Max-Mahalanobis Anchors Guidance for Multi-View Clustering (AAAI 2025 · similarity 0.91)
- A Non-parametric Graph Clustering Framework for Multi-View Data (AAAI 2024 · similarity 0.91)
- Fast Incomplete Multi-view Clustering by Flexible Anchor Learning (ICML 2025 · similarity 0.90)
- Learning Cluster-Wise Anchors for Multi-View Clustering (AAAI 2024 · similarity 0.90)
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