Research map / Learning theory and algorithms / Matrix and tensor methods
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use residual ratio thresholding to choose the OMP support size without known sparsity or noise statistics | OMP · Synthetic sparse recovery, four experiments | l2 error | Helped |
| Use residual ratio thresholding to choose the OMP support size without known sparsity or noise statistics | OMP · Synthetic sparse recovery, four experiments | false positives | Helped |
| Use residual ratio thresholding to choose the OMP support size without known sparsity or noise statistics | OMP · Synthetic sparse recovery, four experiments | false negatives | Helped |
| Use OMP with the noise-based residual stopping condition | OMP · Synthetic sparse recovery, four experiments | false negatives | Hurt |
| Set RRT α to 1/log(n) instead of 1/√n (ablation) | RRT · Synthetic sparse recovery, four experiments | l2 error | No clear effect |
| Set RRT α to 1/log(n) instead of 1/√n (ablation) | RRT · Outlier detection: Stack Loss, AR2000, Brain Body Weight, Stars | outlier identifications | No clear effect |
| Use RRT for outlier detection rather than CV or LAT | Sparse support identification after projecting out the regression signal · Outlier detection: Stack Loss, AR2000, Brain Body Weight, Stars | agreement with outliers reported in literature | Helped |
| Replace RRT with CV for outlier detection | Sparse support identification after projecting out the regression signal · Outlier detection: Stack Loss, AR2000, Brain Body Weight, Stars | false positives | Hurt |
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