Research map / Learning theory and algorithms / Clustering
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use orthogonal matching pursuit instead of basis pursuit for sparse representation | SSC · Synthetic subspace clustering, up to 6,000 points | running time: 3 to 4 orders of magnitude faster | Helped |
| Use orthogonal matching pursuit instead of basis pursuit for sparse representation | SSC · MNIST, 500–6000 points | clustering accuracy: 83.01 -> 83.64; 84.06 -> 86.67; 85.58 -> 90.60; 86.00 -> 91.22; 85.60 -> 91.25 | Helped |
| Use orthogonal matching pursuit instead of basis pursuit for sparse representation | SSC · MNIST, 500–6000 points | subspace-preserving representation error: 29.56 -> 42.13; 24.88 -> 38.73; 21.07 -> 36.20; 17.80 -> 34.22; 16.08 -> 33.22 | Hurt |
| Use orthogonal matching pursuit instead of basis pursuit for sparse representation | SSC · Extended Yale B, 2–38 subjects | clustering accuracy: 99.45 -> 99.18; 91.85 -> 86.09; 79.80 -> 81.55; 76.10 -> 78.27; 68.97 -> 77.59 | Mixed |
| Use orthogonal matching pursuit instead of basis pursuit for sparse representation | SSC · Extended Yale B, 2–38 subjects | running time: 49.1 -> 0.6; 228.2 -> 8.3; 554.6 -> 31.1; 1240 -> 63.7; 1851 -> 108.6 | Helped |
| Use orthogonal matching pursuit instead of least-squares regression | Subspace clustering · MNIST, 500 points | clustering accuracy: 75.84 -> 83.64 | Helped |
| Use orthogonal matching pursuit instead of low-rank subspace clustering | Subspace clustering · MNIST, 500 points | clustering accuracy: 75.02 -> 83.64 | Helped |
| Use orthogonal matching pursuit instead of least-squares regression | Subspace clustering · Extended Yale B, 2 subjects | clustering accuracy: 96.77 -> 99.18 | Helped |
| Use orthogonal matching pursuit instead of low-rank subspace clustering | Subspace clustering · Extended Yale B, 2 subjects | clustering accuracy: 94.32 -> 99.18 | Helped |
| Increase data density | SSC-OMP · Synthetic random subspaces | clustering accuracy | Helped |
Closest papers by meaning
- Sparse Subspace Clustering by Learning Approximation ℓ0 Codes (AAAI 2017 · similarity 0.93)
- Stochastic Sparse Subspace Clustering (CVPR 2020 · similarity 0.90)
- Hyperplane Clustering via Dual Principal Component Pursuit (ICML 2017 · similarity 0.90)
- Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming (CVPR 2016 · similarity 0.89)
- Selective Sampling-based Scalable Sparse Subspace Clustering (NeurIPS 2019 · similarity 0.89)
- Exploring a Principled Framework for Deep Subspace Clustering (ICLR 2025 · similarity 0.87)
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