Research map / Learning theory and algorithms / Prediction and decision losses
Parameter Free Large Margin Nearest Neighbor for Distance Metric Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace LMNN with PFLMNN (drop pull term, use nearest imposter only) | LMNN · Isolet | accuracy (%): 95.83±3.21 -> 97.45±1.21 | Helped |
| replace LMNN with PFLMNN | LMNN · Iris | accuracy (%): 94.23±1.54 -> 96.41±1.23 | Helped |
| replace LMNN with PFLMNN | LMNN · Wine | accuracy (%): 98.36±1.03 -> 98.55±1.67 | No clear effect |
| replace LMNN with PFLMNN | LMNN · USPS | accuracy (%): 98.28±1.25 -> 99.42±1.22 | Helped |
| replace LMNN with PFLMNN | LMNN · AT&T | accuracy (%): 96.81±1.41 -> 97.21±2.34 | No clear effect |
| replace GBLMNN with kernelized PFLMNN | GBLMNN · USPS | accuracy (%): 98.72±0.43 -> 98.62±0.46 | No clear effect |
| replace GBLMNN with kernelized PFLMNN | GBLMNN · Letter | accuracy (%): 96.51±2.34 -> 98.43±1.47 | Helped |
| replace GBLMNN with kernelized PFLMNN | GBLMNN · Coil-100 | accuracy (%): 97.52±1.26 -> 97.43±1.12 | No clear effect |
Closest papers by meaning
- Metric Learning as Convex Combinations of Local Models With Generalization Guarantees (CVPR 2016 · similarity 0.87)
- Active Nearest-Neighbor Learning in Metric Spaces (NeurIPS 2016 · similarity 0.87)
- Large-Scale Distance Metric Learning With Uncertainty (CVPR 2018 · similarity 0.87)
- Curvilinear Distance Metric Learning (NeurIPS 2019 · similarity 0.87)
- SLADE: A Self-Training Framework for Distance Metric Learning (CVPR 2021 · similarity 0.87)
- Learning Distances from Data with Normalizing Flows and Score Matching (ICML 2025 · similarity 0.84)
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