Research map / Learning theory and algorithms / Kernels and regression theory
Group Sparse Additive Machine
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Group sparse additive classifier (GroupSAM) vs. SAM and GroupSpAM with feature-group structure | GroupSAM · Synthetic data (24 groups, σ=0.8) | classification accuracy: 0.953 vs 0.895 (SAM) and 0.880 (GroupSpAM) | Helped |
| Group sparse additive machine (GroupSAM) compared with standard SVM baselines | GroupSAM · Benchmark data (7 UCI datasets) | classification accuracy: Better on Ecoli, Indians Diabetes, Stock, Balance Scale, CMC; slightly lower than SVM on Breast Cancer (0.966 vs 0.968) | Mixed |
| Replicating each feature 3 times to form feature groups for GroupSAM | GroupSAM · Synthetic data (24 groups) | selected groups vs true groups: selected groups exactly match ground truth for all three sigma settings | Helped |
| Group sparsity penalty (ℓ2,1) added to additive classifier (GroupSAM vs SAM) | SAM · Benchmark data (Breast Cancer) | classification accuracy: 0.966 vs 0.833 | Helped |
| Data-dependent kernel hypothesis space and hinge loss (GroupSAM vs GroupSpAM) | GroupSpAM · Benchmark data (Ecoli) | classification accuracy: 0.839 vs 0.771 | Helped |
Closest papers by meaning
- Group Additive Structure Identification for Kernel Nonparametric Regression (NeurIPS 2017 · similarity 0.88)
- Sparse Shrunk Additive Models (ICML 2020 · similarity 0.86)
- Transformer Learns Optimal Variable Selection in Group-Sparse Classification (ICLR 2025 · similarity 0.84)
- Dual Feature Reduction for the Sparse-group Lasso and its Adaptive Variant (ICML 2025 · similarity 0.83)
- Heterogeneous Sufficient Dimension Reduction and Subspace Clustering (ICML 2025 · similarity 0.83)
- Flexible Group Count Enables Hassle-Free Structured Pruning (CVPR 2025 · similarity 0.82)
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