Research map / Learning theory and algorithms / Clustering
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TIPS initialization instead of random initialization for KSS | KSS · Synthetic semi-random union-of-subspaces data, data 3 | convergence to correct clustering | Helped |
| Use TIPS-initialized KSS instead of the strongest accuracy baseline | COIL100 · COIL100 | clustering accuracy: 0.7343 -> 0.8117 | Helped |
| Use KSS instead of the strongest accuracy baseline | YaleB · YaleB | clustering accuracy: 0.8277 -> 0.7154 | Hurt |
| Use KSS instead of the strongest accuracy baseline | USPS · USPS | clustering accuracy: 0.9522 -> 0.8172 | Hurt |
| Use KSS instead of the strongest accuracy baseline | MNIST · MNIST | clustering accuracy: 0.8514 -> 0.9780 | Helped |
Closest papers by meaning
- Stochastic Sparse Subspace Clustering (CVPR 2020 · similarity 0.89)
- Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap (NeurIPS 2022 · similarity 0.89)
- Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph (ICML 2021 · similarity 0.89)
- Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming (CVPR 2016 · similarity 0.88)
- Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit (CVPR 2016 · similarity 0.88)
- Subspace Clustering via Variance Regularized Ridge Regression (CVPR 2017 · similarity 0.88)
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