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Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use collaborative factorization with all six bag, instance, and label relations instead of the compared methods' limited relation sets | M3Lcmf · bag-level datasets overall | AvgF1 | Helped |
| Use collaborative factorization with all six relations rather than MIMLmix | M3Lcmf · instance-level datasets overall | AvgF1 | Mixed |
| Add bag-bag intra-relations (ablation) | M3Lcmf · bag-level datasets | 1-RankLoss | Helped |
| Add instance-instance intra-relations (ablation) | M3Lcmf · bag-level datasets | 1-RankLoss | Helped |
| Add label-label correlations (ablation) | M3Lcmf · bag-level datasets | 1-RankLoss | Helped |
| Add instance-label relations through the aggregation term (ablation) | M3Lcmf · bag-level datasets | 1-RankLoss | Helped |
| Add ten noisy bag intra-relational matrices with selective weighting | M3Lcmf · Azotobacter_vinelandii | performance | No clear effect |
| Add ten noisy bag intra-relational matrices without relevance weighting | MIMLNN · Azotobacter_vinelandii | performance: 2% | Hurt |
| Set either λ1 or λ2 too small rather than in the effective range (ablation) | M3Lcmf · Azotobacter_vinelandii | 1-RankLoss | Hurt |
| Vary λ1 and λ2 over a wide range (ablation) | M3Lcmf · MSRC v2 | 1-RankLoss | No clear effect |
| Use a sufficiently large low-rank size (d≥140 or d≥11) instead of an undersized representation (ablation) | M3Lcmf · Azotobacter_vinelandii and MSRC v2 | 1-RankLoss | Helped |
Closest papers by meaning
- Multi-View Multi-Label Classification via View-Label Matching Selection (AAAI 2025 · similarity 0.87)
- Partial Multi-View Multi-Label Classification via Semantic Invariance Learning and Prototype Modeling (ICML 2024 · similarity 0.86)
- Tensorized Multi-View Multi-Label Classification via Laplace Tensor Rank (ICML 2025 · similarity 0.85)
- T-MDML: Triplet-based Multiple Distance Metric Learning for Multi-Instance Multi-Label Classification with Label Correlation (AAAI 2025 · similarity 0.85)
- Multi-Instance Multi-Label Classification from Crowdsourced Labels (AAAI 2025 · similarity 0.84)
- View-Category Interactive Sharing Transformer for Incomplete Multi-View Multi-Label Learning (CVPR 2024 · similarity 0.84)
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