Research map / Learning theory and algorithms / Clustering
SoS-RSC: A Sum-of-Squares Polynomial Approach to Robustifying Subspace Clustering Algorithms
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SoS-RSC rather than R-graph for outlier detection | MNIST, 2 clusters, 5% outliers · MNIST | AUC: 78.89 vs 81.76 | Hurt |
| Use SoS-RSC rather than R-graph for outlier detection | MNIST, 2 clusters, 5% outliers · MNIST | F1: 35.48 vs 32.33 | Helped |
| Use SoS-RSC rather than R-graph for outlier detection | Extended Yale B, 5 clusters, 15% outliers · Extended Yale B | AUC: 99.67 vs 95.25 | Helped |
| Use SoS-RSC rather than R-graph for outlier detection | Extended Yale B, 5 clusters, 15% outliers · Extended Yale B | F1: 99.31 vs 94.37 | Helped |
| Use SoS-RSC rather than R-graph for outlier detection | Extended Yale B, 10 clusters, 15% outliers · Extended Yale B | AUC: 99.15 vs 91.86 | Helped |
| Use SoS-RSC rather than R-graph for outlier detection | Coil-100, 2 clusters, 25% outliers · Coil-100 | AUC: 98.52 vs 96.20 | Helped |
| Use SoS-RSC rather than R-graph for outlier detection | Coil-100, 2 clusters, 25% outliers · Coil-100 | F1: 98.29 vs 97.13 | Helped |
| Use SoS-RSC rather than R-graph for outlier detection | Caltech-256, 2 clusters, 25% outliers · Caltech-256 | F1: 84.89 vs 86.77 | Hurt |
| Use SoS-RSC rather than R-graph for outlier detection | Hopkins155, 2 clusters, 40% outliers · Hopkins155 | AUC: 99.81 vs 64.81 | Helped |
| Use SoS-RSC rather than R-graph for outlier detection | Hopkins155, 3 clusters, 40% outliers · Hopkins155 | F1: 100.00 vs 43.62 | Helped |
| Use SoS-RSC rather than R-graph for outlier detection | Hopkins155, 5 clusters, 40% outliers · Hopkins155 | AUC: 100.00 vs 26.50 | Helped |
| Use SoS-RSC rather than R-graph for outlier detection | Hopkins155, 5 clusters, 40% outliers · Hopkins155 | F1: 100.00 vs 9.64 | Helped |
Closest papers by meaning
- Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming (CVPR 2016 · similarity 0.91)
- Decentralized Robust Subspace Clustering (AAAI 2016 · similarity 0.88)
- Fixed-Parameter and Approximation Algorithms for PCA with Outliers (ICML 2021 · similarity 0.88)
- Robust Subspace Clustering With Independent and Piecewise Identically Distributed Noise Modeling (CVPR 2019 · similarity 0.87)
- Multi-Subspace Matrix Recovery from Permuted Data (AAAI 2025 · similarity 0.86)
- Robust Sparsification via Sensitivity (ICML 2025 · similarity 0.86)
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