Research map / Label-efficient and robust learning / Semi-supervised learning
Semi-Supervised Dictionary Learning via Structural Sparse Preserving
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use SSP-DL instead of SSR-D (structural sparse preserving between labeled and unlabeled samples) | SSP-DL · MNIST | accuracy (%): 83.83±1.2 -> 85.75±1.2 | Helped |
| use SSP-DL instead of SSR-D | SSP-DL · UMIST | accuracy (%): 87.25±2.7 -> 88.73±2.5 | Helped |
| use SSP-DL instead of SSR-D | SSP-DL · SBData | accuracy (%): 63.98±1.8 -> 64.33±1.4 | No clear effect |
| use SSP-DL instead of SSR-D with few labeled samples | SSP-DL · COIL-20, 2 labeled per class | accuracy (%): 71.99±1.8 -> 74.47±1.7 | Helped |
| use SSP-DL instead of SSR-D with 9 labeled samples | SSP-DL · COIL-20, 9 labeled per class | accuracy (%): 92.10±1.2 -> 91.48±0.7 | Hurt |
| use unlabeled samples in dictionary learning (semi-supervised vs supervised) | dictionary learning · COIL-20 | accuracy (%) | Mixed |
Closest papers by meaning
- Discriminative Semi-Supervised Dictionary Learning with Entropy Regularization for Pattern Classification (AAAI 2017 · similarity 0.91)
- Alternating minimization for dictionary learning with random initialization (NeurIPS 2017 · similarity 0.88)
- Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness (ICLR 2020 · similarity 0.87)
- Orthogonality-Promoting Dictionary Learning via Bayesian Inference (AAAI 2019 · similarity 0.86)
- Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data (NeurIPS 2024 · similarity 0.85)
- On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning (NeurIPS 2026 · similarity 0.84)
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