Research map / Learning theory and algorithms / Prediction and decision losses
Geometric Mean Metric Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the weighted geometric-mean solution with tuned t instead of the ordinary version (t=0.5) (ablation) | GMML · Five UCI datasets shown in Figure [ref] | k-NN classification accuracy | Helped |
| Increase the number of sampled pairs from 40c(c-1) to 1000c(c-1) (ablation) | GMML · Isolet | accuracy: about 1 percent better accuracy | Helped |
| Increase the number of sampled pairs from 40c(c-1) to 1000c(c-1) (ablation) | GMML · MNIST | accuracy: about 0.5 percent better accuracy | Helped |
| Use GMML rather than competing metric-learning methods | GMML · Small UCI datasets | k-NN classification error | Helped |
| Use GMML rather than the best competing method | GMML · Letters and USPS | k-NN classification error | No clear effect |
| Use GMML rather than LMNN | GMML · Large datasets (two unspecified datasets) | k-NN classification error | Mixed |
Closest papers by meaning
- Joint Dimensionality Reduction and Metric Learning: A Geometric Take (ICML 2017 · similarity 0.88)
- Estimating Riemannian Metric with Noise-Contaminated Intrinsic Distance (NeurIPS 2023 · similarity 0.87)
- Learning Low-Dimensional Metrics (NeurIPS 2017 · similarity 0.87)
- Sample-Efficient Geometry Reconstruction from Euclidean Distances using Non-Convex Optimization (NeurIPS 2024 · similarity 0.86)
- Euclidean Embedding of Data Using Local Distances (NeurIPS 2026 · similarity 0.85)
- Learning Distances from Data with Normalizing Flows and Score Matching (ICML 2025 · similarity 0.84)
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