Research map / Learning theory and algorithms / Clustering
Stochastic Sparse Subspace Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply dropout to the self-expressive dictionary | SSC-style subspace clustering · Synthetic data | connectivity | Helped |
| Apply dropout to the self-expressive dictionary | S³COMP-C · Synthetic data | clustering accuracy | No clear effect |
| Increase the dropout rate | S³COMP-C · Synthetic data | subspace-preserving error | Hurt |
| Use consensus iterations rather than only one outer iteration (ablation) | S³COMP-C versus S³COMP · COIL100 | clustering accuracy: 71.47 -> 78.89 | Helped |
| Use consensus iterations rather than only one outer iteration (ablation) | S³COMP-C versus S³COMP · MNIST4000 | clustering accuracy: 94.30 -> 94.27 | No clear effect |
| Use consensus iterations rather than only one outer iteration (ablation) | S³COMP-C versus S³COMP · MNIST10000 | clustering accuracy: 95.73 -> 95.74 | No clear effect |
| Use consensus iterations rather than only one outer iteration (ablation) | S³COMP-C versus S³COMP · MNIST70000 | mean connectivity: 0.1569 -> 0.1569 | No clear effect |
| Use consensus iterations rather than only one outer iteration (ablation) | S³COMP-C versus S³COMP · GTSRB | clustering accuracy: 95.25 -> 95.54 | Helped |
| Use one outer iteration instead of consensus iterations (ablation) | S³COMP · Extended Yale B | clustering accuracy: 87.41 -> 81.61 | Hurt |
Closest papers by meaning
- Selective Sampling-based Scalable Sparse Subspace Clustering (NeurIPS 2019 · similarity 0.91)
- Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap (NeurIPS 2022 · similarity 0.91)
- Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit (CVPR 2016 · similarity 0.90)
- Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming (CVPR 2016 · similarity 0.90)
- Exploring a Principled Framework for Deep Subspace Clustering (ICLR 2025 · similarity 0.88)
- Heterogeneous Sufficient Dimension Reduction and Subspace Clustering (ICML 2025 · similarity 0.88)
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