Research map / Label-efficient and robust learning / Recognition and long-tail
Unsupervised Feature Selection on Networks: A Generative View
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| select 200 features with GFS instead of using all features | K-means clustering · Citeseer | clustering accuracy: 21.0% | Helped |
| GFS instead of RSFS (strongest baseline) with 200 features | unsupervised feature selection · Citeseer, Cora, Wiki | clustering accuracy: 44.5%, 9.2%, 35.2% | Helped |
| use link structure through generative link model in addition to node content | feature selection · Citeseer, Cora, Wiki | clustering accuracy | Helped |
| LUFS social-dimension link regularization on sparse network | UDFS · Citeseer | clustering accuracy | Hurt |
Closest papers by meaning
- Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks (AAAI 2020 · similarity 0.88)
- Unsupervised Feature Selection with Structured Graph Optimization (AAAI 2016 · similarity 0.87)
- Unsupervised Feature Selection Through Group Discovery (AAAI 2026 · similarity 0.87)
- Adaptive Node Feature Selection for Graph Neural Networks (ICML 2026 · similarity 0.87)
- SAND: One-Shot Feature Selection with Additive Noise Distortion (ICML 2025 · similarity 0.86)
- Unsupervised Kernel-based Multi-view Feature Selection with Robust Self-representation and Binary Hashing (AAAI 2025 · similarity 0.83)
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