Research map / Label-efficient and robust learning / Semi-supervised learning
Joint Semi-Supervised Feature Selection and Classification through Bayesian Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| adaptive weighting and elimination of unlabeled samples via left-truncated Gaussian prior | JSFS · G50C with added noisy unlabeled samples | accuracy | Helped |
| add noisy features to data | JSFS vs baselines · G50C | accuracy | Hurt |
| JSFS joint selection and classification vs filter/embedded baselines with SVM | JSFS · 8 high-dimensional datasets | accuracy | Mixed |
| use unlabeled samples (semi-supervised) instead of supervised Fisher score | feature selection · 8 high-dimensional datasets | accuracy | Helped |
Closest papers by meaning
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- Leveraging Local Variance for Pseudo-Label Selection in Semi-supervised Learning (AAAI 2024 · similarity 0.82)
- Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning (NeurIPS 2025 · similarity 0.82)
- Towards a statistical theory of data selection under weak supervision (ICLR 2024 · similarity 0.82)
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