Research map / Label-efficient and robust learning / Recognition and long-tail
Structure Regularized Unsupervised Discriminant Feature Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| select features with Sr-UDFS instead of using all features | k-means clustering · Coil-20 (30% train) | clustering accuracy (%): 48.72 -> 57.20 | Helped |
| select features with Sr-UDFS instead of using all features | k-means clustering · YaleB (30% train) | clustering accuracy (%): 8.44 -> 16.71 | Helped |
| select features with Sr-UDFS instead of using all features | k-means clustering · USPS (30% train) | clustering accuracy (%): 65.56 -> 65.41 | No clear effect |
| select features with Sr-UDFS instead of using all features | k-means clustering · CMUPIE (30% train) | clustering accuracy (%): 9.77 -> 11.91 | Helped |
| Sr-UDFS instead of best baseline selector SPFS | unsupervised feature selection · YaleB (30% train) | NMI (%): 22.33 -> 29.30 | Helped |
| select features with Sr-UDFS instead of using all features | 1-NN classification · Coil-20, YaleB, CMUPIE | classification accuracy | Helped |
| select features with Sr-UDFS instead of using all features | 1-NN classification · USPS | classification accuracy | No clear effect |
Closest papers by meaning
- Unsupervised Feature Selection with Structured Graph Optimization (AAAI 2016 · similarity 0.89)
- Consensus Guided Unsupervised Feature Selection (AAAI 2016 · similarity 0.86)
- Dependence Guided Unsupervised Feature Selection (AAAI 2018 · similarity 0.86)
- Unsupervised Feature Selection Through Group Discovery (AAAI 2026 · similarity 0.85)
- SAND: One-Shot Feature Selection with Additive Noise Distortion (ICML 2025 · similarity 0.84)
- Hypernetworks for Dynamic Feature Selection (NeurIPS 2026 · similarity 0.82)
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