Research map / Learning theory and algorithms / Clustering
Scalable Sequential Spectral Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace in-memory SC variants with SeqSC | spectral clustering · MNIST | clustering accuracy | Helped |
| replace in-memory SC variants with SeqSC | spectral clustering · MNIST | running time | Helped |
| use SeqSC instead of Nystrom | spectral clustering · CovType | accuracy | Helped |
| use K-Means generated anchor points instead of random anchor points (ablation) | SeqSC · MNIST8m | clustering accuracy | Helped |
| use SeqKM instead of SubKM to generate cluster labels (ablation) | SeqSC · MNIST8m | clustering accuracy | Helped |
| increase number of anchor points from 200 to 800 (ablation) | SeqSC · CovType | accuracy | Helped |
Closest papers by meaning
- Compressive Spectral Clustering (ICML 2016 · similarity 0.91)
- A Simple Approach to Automated Spectral Clustering (NeurIPS 2022 · similarity 0.90)
- Pass-Efficient Algorithms for Graph Spectral Clustering (Student Abstract) (AAAI 2024 · similarity 0.90)
- Equivalence between Graph Spectral Clustering and Column Subset Selection (Student Abstract) (AAAI 2024 · similarity 0.88)
- Dynamic Spectral Clustering with Provable Approximation Guarantee (ICML 2024 · similarity 0.88)
- Structure-Aware Spectral Sparsification via Uniform Edge Sampling (NeurIPS 2025 · similarity 0.87)
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