Research map / Label-efficient and robust learning / Recognition and long-tail
Partial Multi-Label Learning by Low-Rank and Sparse Decomposition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| PML-LRS low-rank + sparse label decomposition vs PML-fp | partial multi-label learning · CAL500 | average precision: .459 -> .638 | Helped |
| PML-LRS vs PML-fp | partial multi-label learning · Delicious | average precision: .451 -> .384 | Hurt |
| PML-LRS vs RankSVM | multi-label learning · Medical | average precision: .889 -> .663 | Hurt |
| PML-LRS vs PML-fp | partial multi-label learning · CAL500 | ranking loss: .209 -> .110 | Helped |
Closest papers by meaning
- Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning (CVPR 2026 · similarity 0.93)
- Partial Multi-Label Learning with Noisy Label Identification (AAAI 2020 · similarity 0.92)
- Limited-Supervised Multi-Label Learning with Dependency Noise (AAAI 2024 · similarity 0.88)
- Realistic Evaluation of Deep Partial-Label Learning Algorithms (ICLR 2025 · similarity 0.87)
- Correlation-Induced Label Prior for Semi-Supervised Multi-Label Learning (ICML 2024 · similarity 0.86)
- Addressing Multi-Label Learning with Partial Labels: From Sample Selection to Label Selection (AAAI 2025 · similarity 0.86)
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