Research map / Learning theory and algorithms / Clustering
A Tighter Analysis of Spectral Clustering, and Beyond
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use 8 rather than 24 eigenvectors (ablation) | Spectral clustering · BSDS image segmentation, 24 clusters | Rand Index: 0.77 -> 0.82 | Helped |
| Use 6 rather than 18 eigenvectors (ablation) | Spectral clustering · BSDS image segmentation, 18 clusters | Rand Index: 0.74 -> 0.77 | Helped |
| Use 3 rather than 7 eigenvectors (ablation) | Spectral clustering · BSDS image segmentation, 7 clusters | Rand Index: 0.74 -> 0.76 | Helped |
| Use 8 rather than 13 eigenvectors (ablation) | Spectral clustering · BSDS image segmentation, 13 clusters | Rand Index: 0.77 -> 0.80 | Helped |
| Use 5 rather than 8 eigenvectors (ablation) | Spectral clustering · BSDS image segmentation, 8 clusters | Rand Index: 0.79 -> 0.86 | Helped |
| Use 7 rather than 9 eigenvectors (ablation) | Spectral clustering · BSDS image segmentation, 9 clusters | Rand Index: 0.61 -> 0.69 | Helped |
Closest papers by meaning
- Fast and Simple Spectral Clustering in Theory and Practice (NeurIPS 2023 · similarity 0.96)
- Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract) (AAAI 2024 · similarity 0.93)
- Structure-Aware Spectral Sparsification via Uniform Edge Sampling (NeurIPS 2025 · similarity 0.90)
- Optimal Graph Clustering without Edge Density Signals (NeurIPS 2025 · similarity 0.89)
- Equivalence between Graph Spectral Clustering and Column Subset Selection (Student Abstract) (AAAI 2024 · similarity 0.89)
- Dynamic Spectral Clustering with Provable Approximation Guarantee (ICML 2024 · similarity 0.88)
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