Research map / Learning theory and algorithms / Matrix and tensor methods
Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace TNN (single orientation) with OITNN-O (multi-orientation tubal nuclear norm) | robust tensor decomposition · UGV camera, (s,c)=(0.15,0.15) | PSNR: 25.54 -> 26.12 | Helped |
| replace TNN with OITNN-O | robust tensor decomposition · UGV distance, (s,c)=(0.15,0.15) | PSNR: 25.86 -> 25.97 | No clear effect |
| replace SNN (Tucker-style overlapped norm) with OITNN-O | robust tensor decomposition · UGV camera, (s,c)=(0.3,0.2) | PSNR: 22.55 -> 24.08 | Helped |
| replace LatentNN with OITNN-L | robust tensor decomposition · UGV camera, (s,c)=(0.15,0.15) | PSNR: 23.61 -> 25.79 | Helped |
| use OITNN-L instead of OITNN-O | robust tensor decomposition · UGV, six column settings | PSNR: 26.12 -> 25.79 (camera), 25.97 -> 26.35 (distance) | Mixed |
| use OITNN norms vs competing norms on color images, videos and inpainting | robust tensor recovery · images, videos, inpainting | PSNR | Helped |
| increase OITR complexity and sparsity ratio of the signal | OITNN models · synthetic 40x40x40 tensors | element-wise estimation error | Hurt |
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