Research map / Learning theory and algorithms / Matrix and tensor methods
Efficient Multi-Dimensional Tensor Sparse Coding Using t-Linear Combination
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use 4DTSC-I (t-linear combination tensor sparse coding) instead of LRTA, strongest baseline | 4DTSC-I · Columbia MSI denoising sigma=50 | PSNR (dB): 33.2 -> 33.58 | Helped |
| use 4DTSC-I instead of LRTA | 4DTSC-I · Columbia MSI denoising sigma=5 | PSNR (dB): 44.25 -> 44.91 | Helped |
| use 5DTSC-I with ~1000x smaller dictionary instead of TCSC | 5DTSC-I · OTB50 basketball video reconstruction | PSNR (dB): 40.54 -> 41.31 | Helped |
| use 3DTSC-II instead of baselines at high noise (sigma=100,150,200) | 3DTSC-II · Columbia MSI 3D denoising | PSNR (dB) | Helped |
Closest papers by meaning
- TenSR: Multi-Dimensional Tensor Sparse Representation (CVPR 2016 · similarity 0.88)
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- Non-Convex Tensor Recovery from Local Measurements (AAAI 2025 · similarity 0.87)
- Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration (CVPR 2018 · similarity 0.86)
- The Complexity of Sparse Tensor PCA (NeurIPS 2021 · similarity 0.86)
- Generalized Tensor Decomposition for Understanding Multi-Output Regression under Combinatorial Shifts (NeurIPS 2024 · similarity 0.84)
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