Research map / Learning theory and algorithms / Clustering
The Constrained Laplacian Rank Algorithm for Graph-Based Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn graph with exactly k connected components instead of fixed graph + NCut | graph clustering · COIL100 | ACC: 0.6535 -> 0.8122 | Helped |
| CLR instead of NCut | graph clustering · block diagonal synthetic, noise 0.8 | accuracy: 85% -> 99% | Helped |
| CLR instead of best baseline | graph clustering · 7 benchmarks | NMI: AR 0.7026 -> 0.5909 | Mixed |
| use Frobenius (L2) distance instead of L1 (ablation) | CLR · Yeast | ACC: 0.4158 -> 0.4872 | Helped |
Closest papers by meaning
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- Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract) (AAAI 2024 · similarity 0.89)
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- Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective (NeurIPS 2024 · similarity 0.87)
- Towards Better-than-2 Approximation for Constrained Correlation Clustering (ICML 2025 · similarity 0.87)
- Local Graph Clustering with Noisy Labels (ICLR 2024 · similarity 0.87)
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