Research map / Learning theory and algorithms / Matrix and tensor methods
MPGL: An Efficient Matching Pursuit Method for Generalized LASSO
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace fGFL with MPGL matching pursuit solver | fused LASSO classification · Leukemia | accuracy: 47.22% -> 95.83% | Helped |
| Replace CVX with MPGL | fused LASSO classification · ArrayCGH | training time (s): 1.3213 -> 0.2555 | Helped |
| Use generalized LASSO (MPGL) instead of linear SVM | medical classification · ArrayCGH | accuracy: 77.19% -> 89.47% | Helped |
| Replace fGFL with MPGL | 2D TV image deconvolution · Cameraman | time (s): 1930.2 -> 2.2560 | Helped |
| Replace fGFL with MPGL | 2D TV image deconvolution · Lena and Pepper | PSNR/SSIM: 29.65/0.8742 -> 29.03/0.8691 (Lena) | Mixed |
| Replace fGFL with MPGL | 2D TV image deconvolution · House | PSNR/SSIM: 30.42/0.8343 -> 31.84/0.8505 | Helped |
Closest papers by meaning
- Beyond L1: Faster and Better Sparse Models with skglm (NeurIPS 2022 · similarity 0.87)
- Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit (CVPR 2016 · similarity 0.86)
- Blended Matching Pursuit (NeurIPS 2019 · similarity 0.86)
- Generalized Linear Model Regression under Distance-to-set Penalties (NeurIPS 2017 · similarity 0.85)
- FFTLasso: Large-Scale LASSO in the Fourier Domain (CVPR 2017 · similarity 0.85)
- Sparse Bayesian Learning via Stepwise Regression (ICML 2021 · similarity 0.85)
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