Research map / Learning theory and algorithms / Prediction and decision losses
Landmark Ordinal Embedding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| warm-start STE with LOE | STE · Food relative similarity dataset | runtime: 9.8770 ± 0.2566 -> 8.0432 ± 0.1227; 22% speedup | Helped |
| warm-start STE with LOE | STE · MNIST | clustering purity | Hurt |
| use LOE instead of direct ordinal-embedding optimization | ordinal embedding · Synthetic triplet comparisons | runtime at comparable Procrustes error: around 6 times as long | Helped |
| use LOE instead of conventional ordinal-embedding methods | ordinal embedding · Large-n regime | computational efficiency | Helped |
| use more landmarks | LOE · Ordinal embedding | ranking accuracy per column | Hurt |
| use more landmarks | LOE · Ordinal embedding | LMDS stability | Helped |
Closest papers by meaning
- Scaling Up Ordinal Embedding: A Landmark Approach (ICML 2019 · similarity 0.94)
- Finite Sample Prediction and Recovery Bounds for Ordinal Embedding (NeurIPS 2016 · similarity 0.92)
- On the rankability of visual embeddings (NeurIPS 2025 · similarity 0.85)
- Euclidean Embedding of Data Using Local Distances (NeurIPS 2026 · similarity 0.85)
- A general kernel framework for non-CND distance measures using $|\mathcal{D}|$-dimensional sparse landmark embeddings (NeurIPS 2026 · similarity 0.85)
- Metric Space Magnitude for Evaluating the Diversity of Latent Representations (NeurIPS 2024 · similarity 0.84)
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