Research map / Learning theory and algorithms / Clustering
Product Grassmann Manifold Representation and Its LRR Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| represent image sets as Product Grassmann points with LRR (PGLRR) instead of single Grassmann point (FGLRR) (ablation) | Grassmann LRR · UCSD Traffic | clustering accuracy: 0.7984 -> 0.8379 | Helped |
| represent image sets as Product Grassmann points with LRR (PGLRR) instead of FGLRR (ablation) | Grassmann LRR · SKIG light+dark | clustering accuracy: 0.5185 -> 0.5963 | Helped |
| represent image sets as Product Grassmann points with LRR (PGLRR) instead of FGLRR (ablation) | Grassmann LRR · SKIG fist+index+flat | clustering accuracy: 0.4944 -> 0.5056 | Helped |
| represent image sets as Product Grassmann points with LRR (PGLRR) instead of FGLRR (ablation) | Grassmann LRR · CMU-PIE (CMU-E) | clustering accuracy: 1 -> 1 | No clear effect |
| add kernelization (KPGLRR vs PGLRR) (ablation) | PGLRR · UCSD Traffic | clustering accuracy: 0.8379 -> 0.8458 | Helped |
| add kernelization (KPGLRR vs PGLRR) (ablation) | PGLRR · SKIG fist+index+flat | clustering accuracy: 0.5056 -> 0.5194 | Helped |
| PGLRR/KPGLRR vs other manifold and Euclidean clustering methods | clustering · MNIST with noise images | clustering accuracy: almost 20 percents | Helped |
Closest papers by meaning
- Double Nuclear Norm Based Low Rank Representation on Grassmann Manifolds for Clustering (CVPR 2019 · similarity 0.89)
- Geometric Analysis of Nonlinear Manifold Clustering (NeurIPS 2024 · similarity 0.88)
- Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering (CVPR 2017 · similarity 0.88)
- Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit (ICML 2016 · similarity 0.86)
- From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement (NeurIPS 2024 · similarity 0.85)
- Random features for Grassmannian kernel approximation with bounded rank-one projections (NeurIPS 2026 · similarity 0.85)
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