Research map / Learning theory and algorithms / Matrix and tensor methods
Fast greedy algorithms for dictionary selection with generalized sparsity constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Replacement OMP instead of SDS_OMP or Replacement Greedy | Dictionary selection on synthetic data, T = 100 · Synthetic dataset | running time | Helped |
| Use Replacement OMP instead of SDS_OMP or Replacement Greedy | Dictionary selection on synthetic data, T = 100 · Synthetic dataset | test residual variance | No clear effect |
| Use Replacement OMP instead of SDS_OMP or Replacement Greedy | Dictionary selection on VOC2006, T = 100 · VOC2006 image dataset | test residual variance | Hurt |
| Use Replacement OMP instead of MOD or KSVD | Dictionary selection, T = 1000 · Synthetic dataset and VOC2006 image dataset | running time | Helped |
| Use Replacement OMP instead of MOD or KSVD | Dictionary selection, T = 1000 · Synthetic dataset and VOC2006 image dataset | test residual variance | No clear effect |
| Use Replacement OMP or ROMPd instead of the average-sparsity variant of SDS_MA | Dictionary selection with average sparsity, T = 1000 · Synthetic dataset and VOC2006 image dataset | test residual variance | Helped |
| Use Replacement OMP or ROMPd instead of the average-sparsity variant of SDS_MA | Dictionary selection with average sparsity, T = 1000 · Synthetic dataset and VOC2006 image dataset | running time | Helped |
| Use Online ROMP instead of Online SDS_MA, Online Replacement Greedy, or online dictionary learning | Online dictionary selection · Synthetic and VOC2006 datasets | performance | Helped |
| Replace the fixed smoothness parameter with a parameter that decreases as the dictionary grows (ablation) | Replacement OMP, called ROMPd · Dictionary selection experiments | test residual variance | Mixed |
Closest papers by meaning
- On Learning Sparsely Used Dictionaries from Incomplete Samples (ICML 2018 · similarity 0.88)
- Alternating minimization for dictionary learning with random initialization (NeurIPS 2017 · similarity 0.87)
- Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness (ICLR 2020 · similarity 0.87)
- Global Identifiability of Overcomplete Dictionary Learning via L1 and Volume Minimization (ICLR 2025 · similarity 0.86)
- Semi-Supervised Dictionary Learning via Structural Sparse Preserving (AAAI 2016 · similarity 0.86)
- On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning (NeurIPS 2026 · similarity 0.86)
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