Research map / Learning theory and algorithms / Optimal transport
Co-clustering through Optimal Transport
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CCOT-GW instead of the strongest listed baseline, GLBM | Co-clustering on D3 · D3 | CCE: .262±.022 -> .008±.001 | Helped |
| Use CCOT instead of the strongest listed baseline, GLBM | Co-clustering on D3 · D3 | CCE: .262±.022 -> .031±.027 | Helped |
| Use CCOT-GW instead of the strongest listed baseline, RBC | Co-clustering on D4 · D4 | CCE: .102±.071 -> .079±.031 | Helped |
| Use CCOT instead of the strongest listed baseline, RBC | Co-clustering on D4 · D4 | CCE: .102±.071 -> .093±.032 | Helped |
| Use CCOT-GW instead of the strongest listed baseline, RBC | Co-clustering on D1 · D1 | CCE: .017±.045 -> .004±.002 | Helped |
| Use CCOT instead of the strongest listed baseline, RBC | Co-clustering on D1 · D1 | CCE: .017±.045 -> .018±.013 | No clear effect |
| Use CCOT-GW instead of the strongest listed baseline, GLBM | Co-clustering on D2 · D2 | CCE: .032±.041 -> .011±.056 | Helped |
| Use CCOT instead of the strongest listed baseline, GLBM | Co-clustering on D2 · D2 | CCE: .032±.041 -> .023±.036 | Helped |
Closest papers by meaning
- An Optimal Transport View for Subspace Clustering and Spectral Clustering (AAAI 2024 · similarity 0.88)
- Double-Bounded Optimal Transport for Advanced Clustering and Classification (AAAI 2024 · similarity 0.87)
- Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures (NeurIPS 2024 · similarity 0.85)
- Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling (NeurIPS 2024 · similarity 0.85)
- Learning-Augmented Streaming Algorithms for Correlation Clustering (NeurIPS 2025 · similarity 0.85)
- Hierarchical Refinement: Optimal Transport to Infinity and Beyond (ICML 2025 · similarity 0.84)
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