Research map / Graph learning / Graph algorithms and clustering
Large-Scale Multi-View Subspace Clustering in Linear Time
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LMVSC instead of the strongest listed baseline | LMVSC · Caltech7 | Acc: 0.5604 -> 0.7266 | Helped |
| Use LMVSC instead of the strongest listed baseline | LMVSC · Caltech20 | Acc: 0.4845 -> 0.5306 | Helped |
| Use LMVSC instead of the strongest listed baseline | LMVSC · Handwritten | Acc: 0.8460 -> 0.9165 | Helped |
| Use LMVSC instead of the strongest listed baseline | LMVSC · Reuters | Acc: 0.5063 -> 0.5890 | Helped |
| Use LMVSC instead of the strongest listed baseline | LMVSC · NUS | Acc: 0.1548 -> 0.1553 | Helped |
| Use LMVSC instead of the strongest listed baseline | LMVSC · MNIST with Gaussian noise | Acc: 0.4465 -> 0.5565 | Helped |
| Use LMVSC instead of the strongest listed baseline | LMVSC · MNIST with Speckle noise | Acc: 0.4560 -> 0.5920 | Helped |
| Use LMVSC instead of the strongest listed baseline | LMVSC · MNIST with Salt&Pepper noise | Acc: 0.4816 -> 0.5889 | Helped |
| Apply LMVSC instead of k-means | single-view clustering · RCV1 | Acc: 0.1846 -> 0.1929 | Helped |
| Apply LMVSC instead of k-means | single-view clustering · CoverType | Acc: 0.2505 -> 0.3862 | Helped |
| Increase the regularization parameter alpha (ablation) | LMVSC · Handwritten and MNIST | clustering performance | Hurt |
| Use too many anchors (ablation) | LMVSC · Handwritten and MNIST | clustering performance | Hurt |
Closest papers by meaning
- Learning Cluster-Wise Anchors for Multi-View Clustering (AAAI 2024 · similarity 0.89)
- Robust Consensus Anchor Learning for Efficient Multi-view Subspace Clustering (ICML 2025 · similarity 0.89)
- DVSAI: Diverse View-Shared Anchors Based Incomplete Multi-View Clustering (AAAI 2024 · similarity 0.89)
- Scalable One-Pass Incomplete Multi-View Clustering by Aligning Anchors (AAAI 2025 · similarity 0.89)
- From Spectrum-free towards Baseline-view-free: Double-track Proximity Driven Multi-view Clustering (ICML 2025 · similarity 0.89)
- From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement (NeurIPS 2024 · similarity 0.88)
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