Research map / Graph learning / Graph algorithms and clustering
Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly optimize consensus anchors and the fused graph instead of selecting fixed anchors separately | EOMSC-CA versus AMGL · ORL mtv | ACC: 0.6150 -> 0.6225 | Helped |
| Jointly optimize consensus anchors and the fused graph instead of selecting fixed anchors separately | EOMSC-CA versus MSGL · Caltech101-7 | ACC: 0.7347 -> 0.8351 | Helped |
| Jointly optimize consensus anchors and the fused graph instead of selecting fixed anchors separately | EOMSC-CA versus AMGL · Mfeat | ACC: 0.8262 -> 0.8220 | Hurt |
| Jointly optimize consensus anchors and the fused graph instead of selecting fixed anchors separately | EOMSC-CA versus FPMVS · Caltech101-20 | ACC: 0.6542 -> 0.6404 | Hurt |
| Jointly optimize consensus anchors and the fused graph instead of selecting fixed anchors separately | EOMSC-CA versus FPMVS · Caltech101-all | ACC: 0.2855 -> 0.2232 | Hurt |
| Jointly optimize consensus anchors and the fused graph instead of selecting fixed anchors separately | EOMSC-CA versus FPMVS · SUNRGBD | ACC: 0.2335 -> 0.2370 | Helped |
| Jointly optimize consensus anchors and the fused graph instead of selecting fixed anchors separately | EOMSC-CA versus FPMVS · AWA | ACC: 0.0893 -> 0.0870 | Hurt |
| Jointly optimize consensus anchors and the fused graph instead of selecting fixed anchors separately | EOMSC-CA versus FPMVS · YoutubeFace | ACC: 0.2302 -> 0.2650 | Helped |
| Jointly optimize consensus anchors and the fused graph instead of selecting fixed anchors separately | EOMSC-CA versus AMGL · ORL mtv | NMI: 0.8717 -> 0.8815 | Helped |
| Jointly optimize consensus anchors and the fused graph instead of selecting fixed anchors separately | EOMSC-CA versus SFMC · Caltech101-7 | NMI: 0.5629 -> 0.5219 | Hurt |
| Use a range of anchor counts rather than one fixed count (ablation) | EOMSC-CA · ORL mtv and NUSWIDEOBJ | ACC, NMI, Fscore | No clear effect |
| Continue alternating optimization across iterations (ablation) | EOMSC-CA · ORL mtv and NUSWIDEOBJ | ACC, NMI, Fscore | Helped |
Closest papers by meaning
- Robust Consensus Anchor Learning for Efficient Multi-view Subspace Clustering (ICML 2025 · similarity 0.94)
- Learning Cluster-Wise Anchors for Multi-View Clustering (AAAI 2024 · similarity 0.93)
- Scalable One-Pass Incomplete Multi-View Clustering by Aligning Anchors (AAAI 2025 · similarity 0.92)
- From Spectrum-free towards Baseline-view-free: Double-track Proximity Driven Multi-view Clustering (ICML 2025 · similarity 0.91)
- Anchor Learning with Potential Cluster Constraints for Multi-view Clustering (AAAI 2025 · similarity 0.90)
- Max-Mahalanobis Anchors Guidance for Multi-View Clustering (AAAI 2025 · similarity 0.90)
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