Research map / Learning theory and algorithms / Matrix and tensor methods
Multi-Mode Tensor Space Clustering Based on Low-Tensor-Rank Representation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use 2 modes instead of 1 (ablation) | MMTSC · YUV, p=0.1 | accuracy: 0.7260 -> 0.8820 | Helped |
| Use 2 modes instead of 1 (ablation) | MMTSC · YUV, p=0.1 | NMI: 0.6724 -> 0.8330 | Helped |
| Use 2 modes instead of 1 (ablation) | MMTSC · Coil20, p=0.1 | accuracy: 0.6296 -> 0.6787 | Helped |
| Use 2 modes instead of 1 (ablation) | MMTSC · Coil20, p=0.1 | NMI: 0.6250 -> 0.7005 | Helped |
| Use 2 modes instead of 1 (ablation) | MMTSC · UCSD, p=0.1 | accuracy: 0.5320 -> 0.6311 | Helped |
| Use 2 modes instead of 1 (ablation) | MMTSC · UCSD, p=0.1 | NMI: 0.4912 -> 0.5993 | Helped |
| Use 2 modes instead of 1 (ablation) | MMTSC · Coil20, p=0.5 | accuracy: 0.8125 -> 0.7463 | Hurt |
| Use 2 modes instead of 1 (ablation) | MMTSC · Coil20, p=0.5 | NMI: 0.8325 -> 0.7763 | Hurt |
| Use 2 modes instead of 1 (ablation) | MMTSC · UCSD, p=0.5 | accuracy: 0.8837 -> 0.8717 | Hurt |
| Use 2 modes instead of 1 (ablation) | MMTSC · UCSD, p=0.5 | NMI: 0.9200 -> 0.9161 | Hurt |
Closest papers by meaning
- From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement (NeurIPS 2024 · similarity 0.92)
- Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model (ICLR 2024 · similarity 0.91)
- Large-scale Multi-view Tensor Clustering with Implicit Linear Kernels (CVPR 2025 · similarity 0.91)
- S2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering (CVPR 2024 · similarity 0.89)
- SLR-MVTC: Smooth Low-Rank Multi-View Tensor Clustering (AAAI 2025 · similarity 0.89)
- Low-Rank Kernel Tensor Learning for Incomplete Multi-View Clustering (AAAI 2024 · similarity 0.89)
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