Research map / Learning theory and algorithms / Clustering
Unified Spectral Clustering With Optimal Graph
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| jointly learn the similarity graph, continuous labels, and discrete labels | Unified spectral clustering · benchmark data sets | clustering performance | Helped |
| learn a convex combination of multiple kernels | SCMK · benchmark data sets | clustering performance | Helped |
| use neighbourhood-scale-adaptive node weighting | ClustRF · benchmark data sets | clustering performance | Helped |
| vary α, β, and γ over wide ranges | SCMK on YALE · YALE | accuracy | No clear effect |
Closest papers by meaning
- Twin Learning for Similarity and Clustering: A Unified Kernel Approach (AAAI 2017 · similarity 0.92)
- A Tighter Analysis of Spectral Clustering, and Beyond (ICML 2022 · similarity 0.91)
- Compressive Spectral Clustering (ICML 2016 · similarity 0.90)
- Multiple Kernel Clustering with Kernel k-Means Coupled Graph Tensor Learning (AAAI 2021 · similarity 0.90)
- Fast and Simple Spectral Clustering in Theory and Practice (NeurIPS 2023 · similarity 0.90)
- Equivalence between Graph Spectral Clustering and Column Subset Selection (Student Abstract) (AAAI 2024 · similarity 0.88)
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