Research map / Learning theory and algorithms / Matrix and tensor methods
Outlier-Robust Tensor PCA
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use tensor outlier-robust PCA instead of matrix OR-PCA | FRDUE outlier detection · FRDUE with 100 outliers | AUC: 0.943 -> 0.951 | Helped |
| Use tensor outlier-robust PCA instead of matrix OR-PCA | FRDUE outlier detection · FRDUE with 200 outliers | AUC: 0.886 -> 0.934 | Helped |
| Use tensor outlier-robust PCA instead of R-TPCA | Mnist clustering · Mnist | ACC: 0.781 -> 0.826 | Helped |
| Use tensor outlier-robust PCA instead of R-TPCA | Mnist segmentation · Mnist | ERR: 0.258 -> 0.110 | Helped |
| Use tensor outlier-robust PCA instead of R-TPCA | Semi-supervised classification · FRDUE, 1 training sample per person | classification accuracy (%): 95.6±0.5 -> 97.3±0.6 | Helped |
| Use tensor outlier-robust PCA instead of R-TPCA | Semi-supervised classification · Extended YaleB, 3 training samples per person | classification accuracy (%): 65.1±1.1 -> 71.4±1.1 | Helped |
| Use tensor outlier-robust PCA instead of R-TPCA | Semi-supervised classification · AR, 1 training sample per person | classification accuracy (%): 49.3±1.2 -> 56.8±0.8 | Helped |
| Use 10 randomly selected samples per person for the seed tensor | Fast OR-TPCA · PIE, 10 training samples per person | classification accuracy (%): 83.1±0.8 -> 85.0±0.7 | Helped |
| Use 10 randomly selected samples per person for the seed tensor instead of all samples | Fast OR-TPCA · PIE, 10 training samples per person | total running time (hours): 1.51 -> 0.14 | Helped |
Closest papers by meaning
- Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization (CVPR 2016 · similarity 0.93)
- Towards Multi-Mode Outlier Robust Tensor Ring Decomposition (AAAI 2024 · similarity 0.90)
- Fast and Provable Nonconvex Tensor RPCA (ICML 2022 · similarity 0.90)
- Robust High-Order Tensor Compressive Sensing Based on M-Estimators (AAAI 2026 · similarity 0.89)
- Fun with Flags: Robust Principal Directions via Flag Manifolds (CVPR 2024 · similarity 0.86)
- Deep Rank-One Tensor Functional Factorization for Multi-Dimensional Data Recovery (AAAI 2025 · similarity 0.85)
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