Research map / Learning theory and algorithms / Clustering
One-Step Spectral Clustering via Dynamically Learning Affinity Matrix and Subspace
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| one-step spectral clustering vs CLR | spectral clustering · Umist | ACC: 69.22 -> 79.83 | Helped |
| one-step spectral clustering vs CLR | spectral clustering · Coil | ACC: 85.35 -> 94.72 | Helped |
| one-step spectral clustering vs CLR | spectral clustering · YaleB | ACC: 30.16 -> 45.94 | Helped |
| one-step spectral clustering vs CLR | spectral clustering · Wine | ACC: 72.47 -> 72.47 | No clear effect |
| one-step spectral clustering vs CLR | spectral clustering · Wine | NMI: 39.27 -> 39.27 | No clear effect |
| one-step spectral clustering vs RCut | spectral clustering · Jaffe | ACC: 96.24 -> 96.71 | No clear effect |
| one-step spectral clustering vs CLR | spectral clustering · YaleB | NMI: 45.07 -> 49.70 | Helped |
| one-step spectral clustering vs NCut at high noise | spectral clustering · Synthetic sigma 0.9 | ACC: 85% -> 95% | Helped |
Closest papers by meaning
- RS3CIS: Robust Single-Step Spectral Clustering with Intrinsic Subspace (AAAI 2019 · similarity 0.93)
- A Simple Approach to Automated Spectral Clustering (NeurIPS 2022 · similarity 0.91)
- Sparse Subspace Clustering by Learning Approximation ℓ0 Codes (AAAI 2017 · similarity 0.88)
- Boosting Spectral Clustering on Incomplete Data via Kernel Correction and Affinity Learning (NeurIPS 2023 · similarity 0.88)
- Scalable Sequential Spectral Clustering (AAAI 2016 · similarity 0.87)
- An Optimal Transport View for Subspace Clustering and Spectral Clustering (AAAI 2024 · similarity 0.86)
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