Research map / Learning theory and algorithms / Clustering
Automatic Model Selection in Subspace Clustering via Triplet Relationships
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use triplet relationships with joint model selection reward instead of pairwise-based estimators (SCAMS) | autoSC · extended Yale B, 30 subjects | NCe: 76.22 -> 4.98 | Helped |
| autoSC vs SVD-based K estimation as subjects grow | autoSC · extended Yale B, 30 subjects | NMI: 0.2808 -> 0.8287 | Helped |
| autoSC vs DP-space on clustering quality | autoSC · extended Yale B, 8 subjects | NMI: 0.0343 -> 0.9062 | Helped |
| swap coefficient matrix from SMR to LRR for DP baseline | DP · extended Yale B, 8 subjects | NCe: 3.06 -> 265.60 | Hurt |
| vary coefficient matrix source (LRR, CASS, LSR, SMR, ORGEN) (ablation) | autoSC · extended Yale B and COIL-20 | triplet error rate A: 0.0135 to 0.0181 range (eYaleB) | No clear effect |
| use ORGEN (very sparse) coefficient matrix instead of CASS/LSR/SMR (ablation) | autoSC · extended Yale B, 8 subjects | NCe/NMI | Hurt |
Closest papers by meaning
- Subspace Clustering via Variance Regularized Ridge Regression (CVPR 2017 · similarity 0.87)
- Neural Collaborative Subspace Clustering (ICML 2019 · similarity 0.87)
- Mixed-Features Vectors and Subspace Splitting (ICLR 2021 · similarity 0.87)
- From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement (NeurIPS 2024 · similarity 0.86)
- Exploring a Principled Framework for Deep Subspace Clustering (ICLR 2025 · similarity 0.85)
- Heterogeneous Sufficient Dimension Reduction and Subspace Clustering (ICML 2025 · similarity 0.85)
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