Research map / Learning theory and algorithms / Clustering
Multiple Clustering Views from Multiple Uncertain Experts
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learning expert views and expert accuracies (MCVC) vs. separate semi-supervised clustering on multiple experts | COPD subtyping · COPD subtyping | p-value on genetic variables | Helped |
| Combining multiple expert constraints via SemiCrowd (single consensus solution) | COPD subtyping · COPD subtyping | p-value on genetic variables | Hurt |
| Weighting experts by learned accuracy (MCVC) vs. unweighted experts on synthetic data with unequal accuracies | MCVC · Synthetic unequal accuracies | NMI with ground-truth clustering | Helped |
| Modeling irrelevant experts with learned accuracy parameters (ablation) | MCVC · Synthetic with irrelevant experts | posterior accuracy of irrelevant experts | Helped |
| Meta clustering on expert constraints (MetaClust) instead of MCVC for expert view recovery | MetaClust · Synthetic, WebKB, Face | NMI with ground-truth expert views | Hurt |
| Equal high expert accuracies: MPCKMeans vs MCVC | MCVC · Synthetic/WebKB/Face equal accuracies | NMI | Mixed |
Closest papers by meaning
- Semi-crowdsourced Clustering with Deep Generative Models (NeurIPS 2018 · similarity 0.88)
- Unified and Efficient Multi-view Clustering from Probabilistic Perspective (ICLR 2026 · similarity 0.87)
- Improved Learning-augmented Algorithms for k-means and k-medians Clustering (ICLR 2023 · similarity 0.87)
- Generalizing Fair Clustering to Multiple Groups: Algorithms and Applications (AAAI 2026 · similarity 0.86)
- Mixture of Experts as Representation Learner for Deep Multi-View Clustering (AAAI 2025 · similarity 0.86)
- A Peer-review Look on Multi-modal Clustering: An Information Bottleneck Realization Method (ICML 2025 · similarity 0.86)
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