Research map / Learning theory and algorithms / Clustering
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use KSSCR with the Log-Euclidean Gaussian kernel rather than the strongest reported baseline | Brodatz clustering · Brodatz, 4 clusters | accuracy (%): 79.30 -> 85.94 | Helped |
| Use KSSCR with the Log-Euclidean Gaussian kernel rather than the strongest reported baseline | Brodatz clustering · Brodatz, 8 clusters | accuracy (%): 78.03 -> 97.85 | Helped |
| Use KSSCR with the Log-Euclidean Gaussian kernel rather than the strongest reported baseline | Brodatz clustering · Brodatz, 10 clusters | accuracy (%): 72.36 -> 74.64 | Helped |
| Use KSSCR with the Log-Euclidean Gaussian kernel rather than the strongest reported baseline | Brodatz clustering · Brodatz, 12 clusters | accuracy (%): 82.81 -> 75.7 | Hurt |
| Use KSSCR with the Log-Euclidean Gaussian kernel rather than the strongest reported baseline | Brodatz clustering · Brodatz, 16 clusters | accuracy (%): 64.93 -> 83.62 | Helped |
| Use KSSCR with the Log-Euclidean Gaussian kernel rather than the strongest reported baseline | Brodatz clustering · Brodatz, 4 clusters | NMI (%): 63.84 -> 73.62 | Helped |
| Use KSSCR with the Log-Euclidean Gaussian kernel rather than the strongest reported baseline | Brodatz clustering · Brodatz, 8 clusters | NMI (%): 80.21 -> 95.26 | Helped |
| Use KSSCR with the Log-Euclidean Gaussian kernel rather than the strongest reported baseline | Brodatz clustering · Brodatz, 12 clusters | NMI (%): 79.42 -> 79.05 | No clear effect |
| Use KSSCR with the Log-Euclidean Gaussian kernel rather than the strongest reported baseline | Brodatz clustering · Brodatz, 16 clusters | NMI (%): 66.35 -> 82.38 | Helped |
| Use KSSCR rather than LRSC | FERET clustering · FERET, 7 subsets average | accuracy (%): 78.56 -> 80.42 | Helped |
| Use KSSCR rather than LRSC | FERET clustering · FERET, 7 subsets average | NMI (%): 88.43 -> 88.46 | No clear effect |
| Increase gamma toward about 2×10^-3, then increase it further (ablation) | KSSCR · FERET, 30 clusters | clustering score | Mixed |
Closest papers by meaning
- Sparse Subspace Clustering with Entropy-Norm (ICML 2020 · similarity 0.89)
- Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering (CVPR 2017 · similarity 0.89)
- Theoretical Analysis of Sparse Subspace Clustering with Missing Entries (ICML 2018 · similarity 0.88)
- Selective Sampling-based Scalable Sparse Subspace Clustering (NeurIPS 2019 · similarity 0.87)
- From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement (NeurIPS 2024 · similarity 0.85)
- Geometric Analysis of Nonlinear Manifold Clustering (NeurIPS 2024 · similarity 0.85)
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