Research map / Label-efficient and robust learning / Recognition and long-tail
A Probabilistic Derivation of LASSO and L12-Norm Feature Selections
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use flexible per-class l1,2-norm selection instead of class-shared l2,1-norm / l1,inf-norm | l1,2-norm feature selection · six benchmarks (MNIST, BinAlpha, AT&T, Carcinomas, Lung, TOX) | classification accuracy (KNN/SVM/LR) | Helped |
| l1,2-norm vs exclusive LASSO | l1,2-norm feature selection · BinAlpha and AT&T | classification accuracy: about 1.0% higher | Helped |
| l1,2-norm vs baselines with small feature subsets | l1,2-norm feature selection · top 10 or 20 features | classification accuracy: around 5%-10% better | Helped |
| exclusive LASSO on very high-dimensional features | eLASSO · microarray datasets | classification accuracy | Hurt |
| rank features by |w| instead of strict lambda_m selection | LASSO ranking method · feature selection generally | classification accuracy | Helped |
Closest papers by meaning
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- Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination (ICML 2025 · similarity 0.87)
- SAND: One-Shot Feature Selection with Additive Noise Distortion (ICML 2025 · similarity 0.86)
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