Research map / Learning theory and algorithms / Matrix and tensor methods
Structured Local Minima in Sparse Blind Deconvolution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed ℓ4 objective and initialization to find a shift-truncated kernel | Short-and-sparse blind deconvolution · Synthetic sparse blind deconvolution | recovery error | Helped |
| Use the proposed method rather than the annealing Lasso approach | Ground-truth kernel recovery · Synthetic sparse blind deconvolution with sparse, long observations | recovery error | Helped |
| Use the proposed method rather than the two-stage Lasso approach | Ground-truth kernel recovery · Synthetic sparse blind deconvolution with limited observations | recovery error | Hurt |
| Increase observation length and decrease activation sparsity | Proposed method · Synthetic sparse blind deconvolution | recovery error | Helped |
Closest papers by meaning
- A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution (NeurIPS 2019 · similarity 0.92)
- On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution (CVPR 2017 · similarity 0.92)
- Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere (NeurIPS 2018 · similarity 0.91)
- Sparse blind deconvolution via thresholded Wirtinger flow (NeurIPS 2026 · similarity 0.90)
- Geometry and Symmetry in Short-and-Sparse Deconvolution (ICML 2019 · similarity 0.89)
- The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements (NeurIPS 2025 · similarity 0.85)
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