Research map / Learning theory and algorithms / Matrix and tensor methods
Robust Tensor Factorization With Unknown Noise
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Model noise with a Gaussian mixture rather than use the single weighted tensor factorization model (ablation) | WLRTF · Synthetic tensor, missing-entry prediction at 20%, 40%, and 60% | E3: 1.20e-04 -> 2.03e-08 (20%); 0.51 -> 1.90e-08 (40%); 34.6 -> 1.12e-06 (60%) | Helped |
| Model noise with a Gaussian mixture rather than use the single weighted tensor factorization model (ablation) | WLRTF · Synthetic tensor with Gaussian, sparse, and mixture noise | E3: 32.2 -> 29.2 (Gaussian); 7.08e+02 -> 5.10e+02 (sparse); 6.45e+02 -> 4.59e+02 (mixture) | Helped |
| Use MoG WLRTF instead of CWM LRTF | Facade reconstruction · Facade, small and large mixture noise | PSNR: 24.80 -> 25.65 (small); 22.82 -> 23.69 (large) | Helped |
| Use MoG WLRTF instead of CWM LRTF | Multispectral image restoration · Beers | FSIM: 0.9430 -> 0.9200 | Hurt |
| Use a Gaussian mixture to model noise in tensor factorization | MoG WLRTF · Synthetic tensor recovery and real image restoration | E3 | Helped |
Closest papers by meaning
- Deep Rank-One Tensor Functional Factorization for Multi-Dimensional Data Recovery (AAAI 2025 · similarity 0.88)
- Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data (ICML 2019 · similarity 0.88)
- Outlier-Robust Tensor PCA (CVPR 2017 · similarity 0.88)
- Towards Multi-Mode Outlier Robust Tensor Ring Decomposition (AAAI 2024 · similarity 0.86)
- High-Dimensional Tensor Regression With Oracle Properties (ICML 2025 · similarity 0.86)
- Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model (ICLR 2024 · similarity 0.86)
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