Research map / Learning theory and algorithms / Clustering
New l1-Norm Relaxations and Optimizations for Graph Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace squared-distance spectral relaxation with l1-norm relaxation (NR-RC) | Ratio Cut · Breast cancer (2-way) | clustering accuracy: 0.851 -> 0.913 | Helped |
| replace spectral relaxation with l1-norm relaxation (NR-NC) | Normalized Cut · Breast cancer (2-way) | clustering accuracy: 0.872 -> 0.938 | Helped |
| replace spectral relaxation with l1-norm relaxation (NR-RC) | Ratio Cut · Ecoli (multi-way) | clustering accuracy (%): 44.74 -> 64.35 | Helped |
| replace spectral relaxation with l1-norm relaxation (NR-NC) | Normalized Cut · Ecoli (multi-way) | clustering accuracy (%): 46.81 -> 63.22 | Helped |
| replace spectral relaxation with l1-norm relaxation (NR-NC) | Normalized Cut · COIL20 (multi-way) | clustering accuracy (%): 77.51 -> 78.42 | Helped |
| replace spectral relaxation with l1-norm relaxation (NR-RC) | Ratio Cut · UMIST (multi-way) | NMI (%): 76.45 -> 77.19 | Helped |
Closest papers by meaning
- New l2,1-Norm Relaxation of Multi-Way Graph Cut for Clustering (AAAI 2018 · similarity 0.92)
- Instance-Specific Approximation Ratios for Correlation Clustering and Max-Cut (ICML 2026 · similarity 0.90)
- Equivalence between Graph Spectral Clustering and Column Subset Selection (Student Abstract) (AAAI 2024 · similarity 0.88)
- Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective (NeurIPS 2024 · similarity 0.88)
- A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering (ICML 2024 · similarity 0.87)
- Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract) (AAAI 2024 · similarity 0.87)
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