Research map / Learning theory and algorithms / Clustering
Learning to Link
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| interpolate between single and complete linkage | linkage-based clustering · Rings and Disks | Hamming error: almost 0.2 error | Helped |
| mix the hand-designed stroke metric with the neural-network embedding metric | complete linkage · Omniglot | Hamming error: 0.091 | Helped |
| mix the hand-designed color-histogram metric with the neural-network embedding metric | complete linkage · Places2 | Hamming error: approximately 1% | Helped |
| mix the hand-designed color-histogram metric with the neural-network embedding metric | complete linkage · Places2 Diverse | Hamming error: approximately 3.4% | Helped |
Closest papers by meaning
- An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity (AAAI 2022 · similarity 0.90)
- Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints (AAAI 2021 · similarity 0.89)
- New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering (ICML 2024 · similarity 0.89)
- Low-Distortion Clustering with Ordinal and Limited Cardinal Information (AAAI 2024 · similarity 0.87)
- Approximation Scheme for Weighted Metric Clustering via Sherali-Adams (AAAI 2024 · similarity 0.86)
- Accelerating data-driven algorithm selection for combinatorial partitioning problems (NeurIPS 2025 · similarity 0.86)
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