Research map / Learning theory and algorithms / Clustering
Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · Extended Yale B, 2 subjects | clustering error (%): 1.37±3.15 -> 1.21±2.10 | Helped |
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · Extended Yale B, 3 subjects | clustering error (%): 3.12±6.23 -> 2.40±4.92 | Helped |
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · Extended Yale B, 5 subjects | clustering error (%): 5.65±4.33 -> 3.86±2.82 | Helped |
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · Extended Yale B, 8 subjects | clustering error (%): 4.95±3.36 -> 4.67±3.40 | Helped |
| Use direct nonconvex SSC solved by ADMM instead of convex SSC | SSC · Extended Yale B, 10 subjects | clustering error (%): 4.60±2.59 -> 5.84±3.43 | Hurt |
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · Wine | clustering accuracy: 96.6 -> 97.2 | Helped |
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · USPS | clustering accuracy: 76.8 -> 76.8 | No clear effect |
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · Glass | clustering accuracy: 44.9 -> 45.3 | Helped |
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · Letter | clustering accuracy: 36.4 -> 36.0 | Hurt |
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · Vehicle | clustering accuracy: 73.0 -> 73.4 | Helped |
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · UMIST | clustering accuracy: 65.1 -> 66.1 | Helped |
| Use direct nonconvex SSC solved by ADMM instead of the SSC-PG solver | SSC · PIE | clustering accuracy: 46.8 -> 51.1 | Helped |
Closest papers by meaning
- Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering (CVPR 2017 · similarity 0.90)
- Sparse Quantized Spectral Clustering (ICLR 2021 · similarity 0.89)
- Compressive Spectral Clustering (ICML 2016 · similarity 0.89)
- Equivalence between Graph Spectral Clustering and Column Subset Selection (Student Abstract) (AAAI 2024 · similarity 0.88)
- Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract) (AAAI 2024 · similarity 0.87)
- Structure-Aware Spectral Sparsification via Uniform Edge Sampling (NeurIPS 2025 · similarity 0.86)
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