Research map / Learning theory and algorithms / Prediction and decision losses
Curvilinear Distance Metric Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace straight Mahalanobis measurer lines with curved lines (CDML vs linear LMNN/ITML/ODML) | CDML · UCI datasets (kNN) | classification error rate: MNIST 17.46 -> 8.12 vs LMNN | Helped |
| CDML vs best nonlinear baseline | CDML · Synthetic datasets | classification error rate (%): Concentric Circles 8.98 -> 6.95 | Helped |
| CDML vs Npair/Angular/ODML | CDML · Sonar, Glass | classification error rate: Sonar 15.31 -> 15.40; Glass 22.32 -> 22.12 | No clear effect |
| CDML vs Angular | CDML · Hayes-r | classification error rate: 23.12 -> 25.15 | Hurt |
| CDML vs ODML | CDML · IsoLet | classification error rate: 2.68 -> 3.12 | Hurt |
| CDML vs best baseline | CDML · PubFig, LFW, MVS | AUC: PubFig 0.90928 -> 0.92908 | Helped |
Closest papers by meaning
- Learning Distances from Data with Normalizing Flows and Score Matching (ICML 2025 · similarity 0.87)
- Unsupervised Metric Learning with Synthetic Examples (AAAI 2020 · similarity 0.87)
- Metric Learning as Convex Combinations of Local Models With Generalization Guarantees (CVPR 2016 · similarity 0.87)
- Large-Scale Distance Metric Learning With Uncertainty (CVPR 2018 · similarity 0.87)
- Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis (ICML 2018 · similarity 0.87)
- Volume-Aware Distance for Robust Similarity Learning (ICML 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.