Research map / Graph learning / Graph self-supervised learning
Tri-level Robust Clustering Ensemble with Multiple Graph Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add base-clustering reliability weights (ablation) | TRCE · Lung | ACC: 0.8936 -> 0.9034 | Helped |
| add base-clustering reliability weights (ablation) | TRCE · Tdt2 | ACC: 0.6024 -> 0.6273 | Helped |
| add instance-level self-paced weights (ablation) | TRCE · Lung | ACC: 0.8892 -> 0.9034 | Helped |
| add instance-level self-paced weights (ablation) | TRCE · Tdt2 | ACC: 0.6023 -> 0.6273 | Helped |
| add graph-level noise matrix (ablation) | TRCE · Lung | ACC: 0.6847 -> 0.9034 | Helped |
| add graph-level noise matrix (ablation) | TRCE · Tdt2 | ACC: 0.1806 -> 0.6273 | Helped |
| use tri-level robust ensemble rather than the strongest listed baseline | TRCE · Lung | ACC: 0.8675 -> 0.9034 | Helped |
| use tri-level robust ensemble rather than the strongest listed baseline | TRCE · Tdt2 | ACC: 0.5744 -> 0.6273 | Helped |
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