Research map / Learning theory and algorithms / Matrix and tensor methods
Boosted Sparse and Low-Rank Tensor Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SURF instead of ACS estimation | Sparse and low-rank tensor regression · DTI_fact | RMSE: 2.81±0.24 -> 2.81±0.23 | No clear effect |
| Use SURF instead of ACS estimation | Sparse and low-rank tensor regression · DTI_rk2 | RMSE: 2.90±0.31 -> 2.91±0.32 | No clear effect |
| Use SURF instead of ACS estimation | Sparse and low-rank tensor regression · DTI_sl | RMSE: 2.89±0.38 -> 2.87±0.35 | Helped |
| Use SURF instead of Remurs | Sparse and low-rank tensor regression · DTI_tl | RMSE: 2.84±0.35 -> 2.83±0.32 | No clear effect |
| Use SURF instead of ACS estimation | Sparse and low-rank tensor regression · Combined | RMSE: 2.79±0.31 -> 2.78±0.29 | No clear effect |
| Decrease the stagewise step size (ablation) | SURF · Synthetic 2D data | RMSE | Helped |
| Increase the stagewise step size (ablation) | SURF · Synthetic 2D data | Sparsity of coefficients | Helped |
| Use sparse low-rank unit components rather than other compared tensor methods | Tensor regression · Synthetic 2D data | RMSE | Helped |
| Use SURF instead of ACS | Sparse and low-rank tensor regression · DTI datasets | CPU execution time: 60.8±24.4 -> 1.7±0.2; 63.0±21.6 -> 5.2±0.8; 66.3±44.9 -> 1.5±0.1; 96.7±43.2 -> 3.8±0.5; 463.2±268.1 -> 6.2±0.5 | Helped |
Closest papers by meaning
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- Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks (NeurIPS 2025 · similarity 0.87)
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