Research map / Learning theory and algorithms / Clustering
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use IPM-based clustering instead of clique expansion (CEM) | Submodular hypergraph clustering · 20Newsgroups, Mushroom, Covertype45, Covertype67 | clustering error | Helped |
| Use IPM-based clustering instead of clique expansion (CEM) | Submodular hypergraph clustering · 20Newsgroups, Mushroom, Covertype45, Covertype67 | Cheeger constant | Helped |
| Use submodular rather than homogeneous hyperedge weights (ablation) | IPM clustering · 20Newsgroups | clustering performance | Helped |
| Use submodular rather than homogeneous hyperedge weights (ablation) | IPM clustering · Covertype45 and Covertype67 | clustering performance | Helped |
| Use submodular rather than homogeneous hyperedge weights (ablation) | IPM clustering · Mushroom | clustering performance | No clear effect |
| Increase α (ablation) | IPM clustering with submodular hyperedge weights · Some evaluated datasets | Cheeger constant | Mixed |
| Set α=0.028 (ablation) | IPM clustering with submodular hyperedge weights · Covertype67 | clustering error | Helped |
| Set α=0.028 (ablation) | IPM clustering with submodular hyperedge weights · Covertype67 | Cheeger constant | Helped |
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- Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract) (AAAI 2024 · similarity 0.87)
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