Research map / Learning theory and algorithms / Optimal transport
Sliced Wasserstein Distance for Learning Gaussian Mixture Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| fit the GMM by minimizing sliced Wasserstein distance instead of using EM | GMM fitting · Ring-square-line | initializations achieving optimal negative log-likelihood: 29% -> 100% | Helped |
| fit the GMM by minimizing sliced Wasserstein distance instead of using EM | GMM fitting in the learned embedding · MNIST | component purity: 80.48% -> 86.98% | Helped |
| use Adam instead of RMSProp (ablation) | SW-GMM optimization · GMM fitting experiments | optimization performance | No clear effect |
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