Research map / Learning theory and algorithms / Clustering
Supervised Hierarchical Clustering with Exponential Linkage
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace all-pairs training with MST-based training | Single-linkage HAC · Faces, NP Coref, Rexa, AMINER | dendrogram purity | Helped |
| Match the training procedure to the linkage function | Supervised HAC · Four datasets | dendrogram purity: up to 8 points | Helped |
| Jointly learn the exponential-linkage parameter and pairwise dissimilarity | Exponential Linkage HAC · AMINER | dendrogram purity | Helped |
| Jointly learn the exponential-linkage parameter and pairwise dissimilarity | Exponential Linkage HAC · NP Coref | dendrogram purity | Helped |
| Jointly learn the exponential-linkage parameter and pairwise dissimilarity rather than use all-pairs training | Exponential Linkage HAC · Rexa | pairwise F1: more than 20 points higher F1 | Helped |
| Use all-pairs training instead of the best-performing training procedure | Single-linkage and Exponential Linkage HAC · Rexa, NP Coref, Faces | dendrogram purity: 4-8% | Hurt |
| Use all-pairs training instead of the best-performing training procedure | Average-linkage HAC · Four datasets | dendrogram purity | Hurt |
| Use complete linkage rather than another tested linkage | Supervised HAC · Four datasets | dendrogram purity | Hurt |
| Train with expAlpha rather than all-pairs | Exponential Linkage HAC · Rexa | pairwise F1: more than 20 points higher F1 | Helped |
| Use the best-performing method rather than expPlus | Supervised HAC · AMINER | pairwise F1: ~20 points F1 | Helped |
| Use exponential linkage with alpha = -1 instead of single, average, or complete linkage | HAC with Euclidean distance · Synthetic three-cluster dataset | cluster recovery | Helped |
| Choose an exponential-linkage member after training the dissimilarity function rather than fixing a standard linkage beforehand | Supervised HAC · Rexa, Faces | dendrogram purity | Mixed |
Closest papers by meaning
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- Ultrametric Cluster Hierarchies: I Want ‘em All! (NeurIPS 2025 · similarity 0.86)
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