Research map / Learning theory and algorithms / Clustering
Optimal Margin Distribution Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| cluster with optimal margin distribution (ODMC) instead of maximum margin (LG-MMC) | ODMC · 24 UCI datasets | clustering accuracy: win/tie/loss 9/15/0 vs LG-MMC | Helped |
| use ODMC instead of CPMMC | ODMC · 24 UCI datasets | clustering accuracy: win/tie/loss 17/7/0 | Helped |
| use ODMC instead of IterSVR | ODMC · 24 UCI datasets | clustering accuracy: 18/5/1 | Mixed |
| use ODMC instead of k-means | ODMC · 24 UCI datasets | clustering accuracy: 22/2/0 | Helped |
| use stochastic mirror descent in ODMC instead of cutting-plane training per iteration | ODMC · credit-g, german, optdigits, svmguide3, sick, splice | time per iteration | Helped |
Closest papers by meaning
- Multi-Class Optimal Margin Distribution Machine (ICML 2017 · similarity 0.86)
- Maximum Margin Dirichlet Process Mixtures for Clustering (AAAI 2016 · similarity 0.85)
- Coreset Stochastic Variance-Reduced Gradient with Application to Optimal Margin Distribution Machine (AAAI 2019 · similarity 0.85)
- Hierarchical Clustering Beyond the Worst-Case (NeurIPS 2017 · similarity 0.84)
- Multi-Class Support Vector Machine with Maximizing Minimum Margin (AAAI 2024 · similarity 0.83)
- Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling (NeurIPS 2026 · similarity 0.83)
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