Research map / Learning theory and algorithms / Matrix and tensor methods
Adaptive Rank Estimate in Robust Principal Component Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ARE-RPCA instead of WNNM | RPCA on synthetic data · Synthetic data | PSNR | Helped |
| Use ARE-RPCA instead of other compared algorithms | RPCA on synthetic data · Synthetic data | PSNR | Helped |
| Use ARE-RPCA instead of WNNM | RPCA on synthetic data with large sample size and rank · Synthetic data | PSNR | Hurt |
| Use ARE-RPCA instead of the compared algorithms | RPCA on synthetic data · Synthetic data | low-rank reconstruction error | Helped |
| Use ARE-RPCA instead of the compared algorithms | RPCA on synthetic data · Synthetic data | sparse reconstruction error | No clear effect |
| Use ARE-RPCA instead of the compared algorithms | RPCA on synthetic data · Synthetic data | rank recovery | Helped |
| Use ARE-RPCA instead of WNNM | Video background subtraction · Street | foreground-background separation | Helped |
| Use ARE-RPCA instead of WNNM | Low-dynamic-range imaging · Arch | ghosting removal | Helped |
Closest papers by meaning
- Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition (CVPR 2023 · similarity 0.92)
- RES-PCA: A Scalable Approach to Recovering Low-Rank Matrices (CVPR 2019 · similarity 0.91)
- A Nonconvex Projection Method for Robust PCA (AAAI 2019 · similarity 0.87)
- Robust Principal Component Analysis with Adaptive Neighbors (NeurIPS 2019 · similarity 0.86)
- Beyond RPCA: Flattening Complex Noise in the Frequency Domain (AAAI 2017 · similarity 0.86)
- LERE: Learning-Based Low-Rank Matrix Recovery with Rank Estimation (AAAI 2024 · similarity 0.84)
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