Research map / Learning theory and algorithms / Clustering
Robust Subspace Clustering With Independent and Piecewise Identically Distributed Noise Modeling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MWEE-S with i.p.i.d. noise modeling instead of the strongest listed baseline | Subspace clustering · Extended Yale B, 2 subjects | clustering accuracy: 99.22 -> 100.0 | Helped |
| Use MWEE-S with i.p.i.d. noise modeling instead of the strongest listed baseline | Subspace clustering · Extended Yale B, 4 subjects | clustering accuracy: 99.22 -> 100.0 | Helped |
| Use MWEE-S with i.p.i.d. noise modeling instead of the strongest listed baseline | Subspace clustering · Extended Yale B, 6 subjects | clustering accuracy: 98.44 -> 100.0 | Helped |
| Use MWEE-S with i.p.i.d. noise modeling instead of the strongest listed baseline | Subspace clustering · Extended Yale B, 8 subjects | clustering accuracy: 97.66 -> 100.0 | Helped |
| Use MWEE-S with i.p.i.d. noise modeling instead of the strongest listed baseline | Subspace clustering · Extended Yale B, 10 subjects | clustering accuracy: 96.56 -> 99.84 | Helped |
| Use MWEE-S with i.p.i.d. noise modeling instead of LRR | Motion segmentation · Hopkins155, 2F-dimensional data | average clustering error: 1.60 -> 1.22 | Helped |
| Use MWEE-S with i.p.i.d. noise modeling instead of LRR | Motion segmentation · Hopkins155, 4K-dimensional data | average clustering error: 2.35 -> 1.77 | Helped |
Closest papers by meaning
- Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming (CVPR 2016 · similarity 0.89)
- Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap (NeurIPS 2022 · similarity 0.89)
- Stochastic Sparse Subspace Clustering (CVPR 2020 · similarity 0.89)
- Decentralized Robust Subspace Clustering (AAAI 2016 · similarity 0.88)
- Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures (NeurIPS 2024 · similarity 0.87)
- Heterogeneous Sufficient Dimension Reduction and Subspace Clustering (ICML 2025 · similarity 0.85)
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