Research map / Learning theory and algorithms / Clustering
Double Nuclear Norm Based Low Rank Representation on Grassmann Manifolds for Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add adaptive Laplacian regularization and learn the affinity matrix (ablation) | G-DNLR · Extended Yale B | ACC: 0.9749 -> 0.9855 | Helped |
| Add adaptive Laplacian regularization and learn the affinity matrix (ablation) | G-DNLR · CMU-PIE | ACC: 0.6618 -> 0.7244 | Helped |
| Add adaptive Laplacian regularization and learn the affinity matrix (ablation) | G-DNLR · Ballet | ACC: 0.6768 -> 0.6885 | Helped |
| Add adaptive Laplacian regularization and learn the affinity matrix (ablation) | G-DNLR · SKIG | ACC: 0.6244 -> 0.6667 | Helped |
| Add adaptive Laplacian regularization and learn the affinity matrix (ablation) | G-DNLR · Extended Yale B | NMI: 0.9874 -> 0.9891 | Helped |
| Add adaptive Laplacian regularization and learn the affinity matrix (ablation) | G-DNLR · CMU-PIE | NMI: 0.8510 -> 0.8774 | Helped |
| Add adaptive Laplacian regularization and learn the affinity matrix (ablation) | G-DNLR · Ballet | NMI: 0.6310 -> 0.6692 | Helped |
| Add adaptive Laplacian regularization and learn the affinity matrix (ablation) | G-DNLR · SKIG | NMI: 0.6482 -> 0.6484 | Helped |
Closest papers by meaning
- Product Grassmann Manifold Representation and Its LRR Models (AAAI 2016 · similarity 0.89)
- Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering (CVPR 2017 · similarity 0.88)
- Double Low-Rank Representation With Projection Distance Penalty for Clustering (CVPR 2021 · similarity 0.88)
- Geometric Analysis of Nonlinear Manifold Clustering (NeurIPS 2024 · similarity 0.85)
- Random features for Grassmannian kernel approximation with bounded rank-one projections (NeurIPS 2026 · similarity 0.84)
- From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement (NeurIPS 2024 · similarity 0.84)
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