Research map / Learning theory and algorithms / Matrix and tensor methods
Missing Slice Recovery for Tensors Using a Low-Rank Model in Embedded Space
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use multi-way delay embedding and low-rank Tucker completion instead of the strongest listed baseline | Tensor completion · facade 1 | PSNR: 30.18 -> 36.52 | Helped |
| Use multi-way delay embedding and low-rank Tucker completion instead of SPCQV | Tensor completion · facade 2 | PSNR: 25.65 -> 26.96 | Helped |
| Use multi-way delay embedding and low-rank Tucker completion instead of SPCQV | Tensor completion · house | PSNR: 27.04 -> 27.50 | Helped |
| Use multi-way delay embedding and low-rank Tucker completion instead of SPCQV | Tensor completion · peppers | PSNR: 26.59 -> 27.62 | Helped |
| Use multi-way delay embedding and low-rank Tucker completion instead of SPCQV | Tensor completion · Lena (5%) | PSNR: 23.52 -> 23.57 | Helped |
| Use multi-way delay embedding and low-rank Tucker completion instead of SPCQV | Tensor completion · Lena (1%) | PSNR: 18.49 -> 19.68 | Helped |
| Use the proposed method instead of SPCQV | fMRI completion · 1 missing slice | SNR: 24.20 -> 23.25 | Hurt |
| Use the proposed method instead of SPCQV | fMRI completion · 2 missing slices | SNR: 17.80 -> 18.45 | Helped |
| Use the proposed method instead of SPCQV | fMRI completion · 3 missing slices | SNR: 16.58 -> 14.54 | Hurt |
| Use the proposed method instead of SPCQV | fMRI completion · 4 missing slices | SNR: 12.98 -> 14.08 | Helped |
| Use the proposed method instead of SPCQV | fMRI completion · 5 missing slices | SNR: 12.15 -> 13.85 | Helped |
| Model data as low-rank after multi-way delay embedding rather than only in signal space | Tensor completion with missing slices · Color images and fMRI slices | recovery quality | Helped |
Closest papers by meaning
- Fast Algorithm for Low-Rank Tensor Completion in Delay-Embedded Space (CVPR 2022 · similarity 0.89)
- Low-Rank Tensor Completion With a New Tensor Nuclear Norm Induced by Invertible Linear Transforms (CVPR 2019 · similarity 0.89)
- Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion (AAAI 2019 · similarity 0.88)
- Nonconvex Low-Rank Tensor Completion from Noisy Data (NeurIPS 2019 · similarity 0.88)
- Deep Rank-One Tensor Functional Factorization for Multi-Dimensional Data Recovery (AAAI 2025 · similarity 0.87)
- Fast Tensor Completion via Approximate Richardson Iteration (ICML 2025 · similarity 0.86)
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