Research map / Learning theory and algorithms / Clustering
Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Optimize the sparse spectral clustering objective directly on the Grassmann manifold instead of solving the convex SSC relaxation | Spectral clustering · YaleB | accuracy: 95.64(5.90) -> 96.58(3.94) at K = 5; 82.86(4.86) -> 85.24(4.34) at K = 10; 75.06(1.59) -> 76.84(1.49) at K = 18 | Helped |
| Optimize the sparse spectral clustering objective directly on the Grassmann manifold instead of solving the convex SSC relaxation | Spectral clustering · ORL | accuracy: 97.25(6.62) -> 97.80(5.41) at K = 5; 80.95(10.67) -> 82.77(6.32) at K = 10; 77.95(7.41) -> 78.96(5.21) at K = 18 | Helped |
| Use Grassmann manifold optimization rather than the convex SSC solution | Spectral clustering · Three-Gaussian | accuracy: 100 -> 100 | No clear effect |
| Use Grassmann manifold optimization rather than the convex SSC solution | Spectral clustering · Three-Ring | accuracy: 100 -> 100 | No clear effect |
| Use Grassmann manifold optimization rather than the convex SSC solution | Spectral clustering · Two disjoint para-curves | accuracy: 83.50% -> 100% | Helped |
| Increase β to 0.0005 (ablation) | GSC and SSC · Three-Gaussian | accuracy: GSC: 100 -> 95.7; SSC: 100 -> 53 | Mixed |
| Increase β to 0.0005 (ablation) | GSC and SSC · Three-Ring | accuracy: GSC: 100 -> 87.14; SSC: 100 -> 49.43 | Mixed |
| Increase β to 0.01 (ablation) | GSC and SSC · YaleB, K=10 | accuracy: GSC: 83.30 -> 55.00; SSC: 82.24 -> 22.37 | Mixed |
| Increase β to 0.01 (ablation) | GSC and SSC · ORL, K=10 | accuracy: GSC: 81.30 -> 77.10; SSC: 79.90 -> 59.80 | Mixed |
Closest papers by meaning
- Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis (AAAI 2018 · similarity 0.90)
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- Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds (CVPR 2016 · similarity 0.89)
- SpectralNet: Spectral Clustering using Deep Neural Networks (ICLR 2018 · similarity 0.88)
- Double Nuclear Norm Based Low Rank Representation on Grassmann Manifolds for Clustering (CVPR 2019 · similarity 0.88)
- Equivalence between Graph Spectral Clustering and Column Subset Selection (Student Abstract) (AAAI 2024 · similarity 0.86)
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