Research map / Learning theory and algorithms / Optimal transport
Sliced Wasserstein Kernels for Probability Distributions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Sliced Wasserstein Gaussian or polynomial kernels instead of linear or RBF kernels | SVM classification on UIUC texture and LHI animal face · UIUC texture and LHI animal face | classification accuracy | Helped |
| Use Sliced Wasserstein kernel PCA instead of PCA on the input distributions | UIUC texture and LHI animal face · UIUC texture and LHI animal face | cumulative percent variance | Helped |
| Use Sliced Wasserstein Gaussian kernel k-means instead of k-means on the input distributions | UIUC texture and LHI animal face · UIUC texture and LHI animal face | V-measure | Helped |
| Use Sliced Wasserstein Gaussian kernel k-means instead of k-means on the input distributions | UIUC texture and LHI animal face · UIUC texture and LHI animal face | within-cluster sum-of-squares (inertia) | Helped |
Closest papers by meaning
- Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances (ICML 2025 · similarity 0.92)
- Sliced Gromov-Wasserstein (NeurIPS 2019 · similarity 0.91)
- Tree-Sliced Variants of Wasserstein Distances (NeurIPS 2019 · similarity 0.91)
- Towards Understanding Gradient Dynamics of the Sliced-Wasserstein Distance via Critical Point Analysis (ICML 2025 · similarity 0.89)
- Expected Sliced Transport Plans (ICLR 2025 · similarity 0.89)
- Differentiable Generalized Sliced Wasserstein Plans (NeurIPS 2025 · similarity 0.88)
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