Research map / Label-efficient and robust learning / Recognition and long-tail
Dependence Guided Unsupervised Feature Selection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| DGUFS joint feature selection and clustering vs best two-step baseline EUFS | unsupervised feature selection · UMIST | ACC: 51.5 -> 57.1 | Helped |
| DGUFS vs EUFS | unsupervised feature selection · 6 datasets | NMI: 53.0 -> 60.2 (LUNG) | Helped |
| feature selection vs using all features | clustering · PIE10P | ACC: 30.8 -> 51.9 | Helped |
| varying beta and alpha hyperparameters (ablation) | DGUFS · PIX10P | ACC | No clear effect |
Closest papers by meaning
- Consensus Guided Unsupervised Feature Selection (AAAI 2016 · similarity 0.90)
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- Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data (NeurIPS 2024 · similarity 0.83)
- Towards a statistical theory of data selection under weak supervision (ICLR 2024 · similarity 0.82)
- Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond (ICML 2024 · similarity 0.82)
- Most Influential Subset Selection: Challenges, Promises, and Beyond (NeurIPS 2024 · similarity 0.82)
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