Research map / Learning theory and algorithms / Clustering
Adaptive Graph Convolutional Subspace Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add thresholding post-processing to AGCSC (ablation) | AGCSC · ORL | ACC (%): 80.50 -> 86.25 | Helped |
| Add thresholding post-processing to AGCSC (ablation) | AGCSC · YALEB | ACC (%): 84.79 -> 92.31 | Helped |
| Add thresholding post-processing to AGCSC (ablation) | AGCSC · Umist | ACC (%): 81.04 -> 90.83 | Helped |
| Add thresholding post-processing to AGCSC (ablation) | AGCSC · COIL20 | ACC (%): 88.75 -> 98.96 | Helped |
| Add thresholding post-processing to AGCSC (ablation) | AGCSC · COIL40 | ACC (%): 78.12 -> 92.60 | Helped |
| Add thresholding post-processing to AGCSC (ablation) | AGCSC · MNIST | ACC (%): 71.40 -> 72.80 | Helped |
| Use relatively small alpha and beta values (ablation) | AGCSC · All six benchmark datasets | ACC and NMI | Helped |
| Use relatively small thresholding value m (ablation) | TAGCSC · All datasets except MNIST | Clustering performance | Helped |
| Vary thresholding value m (ablation) | TAGCSC · MNIST | Clustering performance | No clear effect |
Closest papers by meaning
- Scalable Attributed-Graph Subspace Clustering (AAAI 2023 · similarity 0.91)
- LRSC: Learning Representations for Subspace Clustering (AAAI 2021 · similarity 0.88)
- Self-Supervised Convolutional Subspace Clustering Network (CVPR 2019 · similarity 0.88)
- Exploring a Principled Framework for Deep Subspace Clustering (ICLR 2025 · similarity 0.87)
- Selective Sampling-based Scalable Sparse Subspace Clustering (NeurIPS 2019 · similarity 0.87)
- Subspace Clustering via Tangent Cones (NeurIPS 2017 · similarity 0.87)
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