Research map / Probabilistic and scientific modeling / Variational and generative sampling
Generative Label Enhancement with Gaussian Mixture and Partial Ranking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GLEMR’s Gaussian-mixture feature generator instead of a single Gaussian (ablation) | GLEMR · 14 real-world label-enhancement datasets | recovery performance | Mixed |
| Use the partial-ranking LD(y|d) likelihood instead of Bernoulli or Gaussian likelihoods (ablation) | GLEMR · 14 real-world label-enhancement datasets | recovery performance | Helped |
| Use GLEMR rather than the compared label-enhancement methods | Label distribution recovery · 14 real-world datasets | average rank: 1.4 vs 4.2 (Cheb); 1.2 vs 4.2 (KL); 1.2 vs 4.1 (Cosine); 1.3 vs 4.0 (Intersec) | Helped |
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