Research map / Learning theory and algorithms / Matrix and tensor methods
Robust Ordinal Embedding from Contaminated Relative Comparisons
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly detect outliers and learn the embedding with a global inconsistency cost | Ordinal embedding on the synthetic dataset · Synthetic dataset, clean | classification error: 0.1884 -> 0.0961 | Helped |
| Jointly detect outliers and learn the embedding with a global inconsistency cost | Ordinal embedding on the synthetic dataset · Synthetic dataset, contaminated (25% outliers) | classification error: 0.1632 -> 0.1025 | Helped |
| Jointly detect outliers and learn the embedding with a global inconsistency cost | Ordinal embedding on music artists · Music artists, clean | classification error: 0.2358 -> 0.2312 | Helped |
| Jointly detect outliers and learn the embedding with a global inconsistency cost | Ordinal embedding on music artists · Music artists, contaminated (25% outliers) | classification error: 0.2120 -> 0.2287 | Hurt |
| Vary the regularization parameter λ (ablation) | ROE · Synthetic dataset | classification error | No clear effect |
| Vary the regularization parameter λ (ablation) | ROE · Music artists | classification error | No clear effect |
Closest papers by meaning
- Finite Sample Prediction and Recovery Bounds for Ordinal Embedding (NeurIPS 2016 · similarity 0.90)
- Scaling Up Ordinal Embedding: A Landmark Approach (ICML 2019 · similarity 0.89)
- Scalable and Interpretable Representation Alignment with Ordinal Similarity (ICML 2026 · similarity 0.89)
- Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin (AAAI 2019 · similarity 0.88)
- Landmark Ordinal Embedding (NeurIPS 2019 · similarity 0.88)
- SLACE: A Monotone and Balance-Sensitive Loss Function for Ordinal Regression (AAAI 2025 · similarity 0.85)
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