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Robust Metric Learning on Grassmann Manifolds with Generalization Guarantees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add robust MLE-like reweighting on Grassmann manifold to LMNN | LMNN · PubFig subset | accuracy (%): 77.33 -> 82.75 | Helped |
| use RLMNN instead of best regularized LMNN (Cap) | LMNN Cap · OSR | accuracy (%): 76.51 -> 78.83 (table); text says 78.1523 | Mixed |
| use RLMNN instead of LMNN Fantope | LMNN Fantope · Highway Traffic | accuracy (%): 91.34 -> 94.35 | Helped |
| use RLMNN instead of LMNN Cap | LMNN Cap · LFW Attribute | 1-EER: 0.845 -> 0.849 | Helped |
| use RLMNN instead of LMNN Cap | LMNN Cap · YouTube Faces | 1-EER: 0.698 -> 0.720 | Helped |
| add matrix-norm regularization (Cap) to LMNN | LMNN · OSR | accuracy (%): 75.32 -> 76.51 | Helped |
Closest papers by meaning
- Fixed-Rank Supervised Metric Learning on Riemannian Manifold (AAAI 2016 · similarity 0.88)
- Learning Low-Dimensional Metrics (NeurIPS 2017 · similarity 0.87)
- Joint Dimensionality Reduction and Metric Learning: A Geometric Take (ICML 2017 · similarity 0.87)
- Provably Robust Metric Learning (NeurIPS 2020 · similarity 0.86)
- Learning Distances from Data with Normalizing Flows and Score Matching (ICML 2025 · similarity 0.84)
- Random features for Grassmannian kernel approximation with bounded rank-one projections (NeurIPS 2026 · similarity 0.84)
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