Research map / Learning theory and algorithms / Clustering
Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| adding bi-level weighting of imputed incomplete samples (DSIMVC vs. baselines) | DSIMVC · BDGP, MNIST-USPS, CCV, Multi-Fashion | ACC | Helped |
| adding bi-level weighting of imputed samples, robustness at high missing ratio | DSIMVC · BDGP, missing ratio 0.5 to 0.7 | ACC drop: DSIMVC ACC drops 3.2% vs 10.2% for second best | Helped |
| DSIMVC vs. variant trained only on complete data (ablation) | DSIMVC · BDGP, complete samples | clustering performance (NMI) | Helped |
| dynamic semantic-neighbor imputation | DSIMVC · MNIST-USPS | semantic consistency ratio of imputations | Helped |
| hyperparameter variation (k from 3 to 7, gamma from 0.3 to 0.7) (ablation) | DSIMVC · NMI sensitivity | NMI | No clear effect |
Closest papers by meaning
- Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding (AAAI 2024 · similarity 0.91)
- Towards Resource-friendly, Extensible and Stable Incomplete Multi-view Clustering (ICML 2024 · similarity 0.91)
- Adaptive Feature Imputation with Latent Graph for Deep Incomplete Multi-View Clustering (AAAI 2024 · similarity 0.91)
- Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures (AAAI 2024 · similarity 0.91)
- Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion (NeurIPS 2025 · similarity 0.90)
- Incomplete Multi-view Clustering via Diffusion Contrastive Generation (AAAI 2025 · similarity 0.90)
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