Research map / Learning theory and algorithms / Clustering
Compressive Spectral Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace k-means on all N nodes with k-means on O(k log k) sampled nodes, with interpolation (CSC) | Spectral clustering · Amazon co-purchasing network | modularity and computation time | No clear effect |
| Replace eigendecomposition and full k-means with compressive filtering, sampling and interpolation (CSC) | Spectral clustering on stochastic block model · SBM, N up to 10^5 | Adjusted Rand index and computation time | No clear effect |
| Use power method instead of eigendecomposition for feature extraction | Spectral clustering on Amazon network · Amazon co-purchasing network | computation time | Hurt |
| Use CSC instead of SC for large k (k=200) | SBM clustering time · SBM, N up to 10^5 | computation time: up to an order of magnitude faster at k=200 | Helped |
| Use CSC instead of SC for small k (k=20) | SBM clustering time · SBM, k=20 | computation time | Hurt |
Closest papers by meaning
- A Tighter Analysis of Spectral Clustering, and Beyond (ICML 2022 · similarity 0.93)
- Fast and Simple Spectral Clustering in Theory and Practice (NeurIPS 2023 · similarity 0.92)
- Fast Approximate Spectral Clustering for Dynamic Networks (ICML 2018 · similarity 0.91)
- Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract) (AAAI 2024 · similarity 0.91)
- Structure-Aware Spectral Sparsification via Uniform Edge Sampling (NeurIPS 2025 · similarity 0.90)
- Equivalence between Graph Spectral Clustering and Column Subset Selection (Student Abstract) (AAAI 2024 · similarity 0.89)
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