Research map / Learning theory and algorithms / Clustering
Auto-Weighted Multi-View Clustering for Large-Scale Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use multiple embedding dimensions rather than a single dimension k (ablation) | AWMVC · Flower17 | ACC: 46.53 -> 49.12 | Helped |
| use discriminatory α weights rather than equal embedding weights (ablation) | AWMVC · Flower17 | ACC: 48.03 -> 49.12 | Helped |
| use multiple embedding dimensions rather than a single dimension k (ablation) | AWMVC · AwA | ACC: 9.44 -> 9.42 | Hurt |
| use discriminatory α weights rather than equal embedding weights (ablation) | AWMVC · AwA | ACC: 9.32 -> 9.42 | Helped |
| use multiple embedding dimensions rather than a single dimension k (ablation) | AWMVC · Caltech256 | ACC: 11.68 -> 12.75 | Helped |
| use discriminatory α weights rather than equal embedding weights (ablation) | AWMVC · Caltech256 | ACC: 12.38 -> 12.75 | Helped |
| use multiple embedding dimensions rather than a single dimension k (ablation) | AWMVC · MNIST | ACC: 96.84 -> 98.85 | Helped |
| use discriminatory α weights rather than equal embedding weights (ablation) | AWMVC · MNIST | ACC: 98.15 -> 98.85 | Helped |
| use multiple embedding dimensions rather than a single dimension k (ablation) | AWMVC · VGGFace2 | ACC: 6.53 -> 6.84 | Helped |
| use discriminatory α weights rather than equal embedding weights (ablation) | AWMVC · VGGFace2 | ACC: 6.39 -> 6.84 | Helped |
| use multiple embedding dimensions rather than a single dimension k (ablation) | AWMVC · TinyImageNet | ACC: 5.02 -> 5.17 | Helped |
| use discriminatory α weights rather than equal embedding weights (ablation) | AWMVC · TinyImageNet | ACC: 5.05 -> 5.17 | Helped |
Closest papers by meaning
- Multi-View Multiple Clusterings Using Deep Matrix Factorization (AAAI 2020 · similarity 0.90)
- A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective (NeurIPS 2023 · similarity 0.89)
- Fast Incomplete Multi-view Clustering by Flexible Anchor Learning (ICML 2025 · similarity 0.88)
- Towards Resource-friendly, Extensible and Stable Incomplete Multi-view Clustering (ICML 2024 · similarity 0.88)
- A Non-parametric Graph Clustering Framework for Multi-View Data (AAAI 2024 · similarity 0.87)
- DVSAI: Diverse View-Shared Anchors Based Incomplete Multi-View Clustering (AAAI 2024 · similarity 0.87)
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