Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Integrate additional knowledge through weighted sparsity constraints for shared and task-specific graph structures | JEEK · Simulated brain connectivity with distance knowledge | F1-score | Helped |
| Use additional distance knowledge | JEEK · ABIDE autism vs control | classification accuracy | Helped |
| Represent known co-hub or perturbed-hub structure with knowledge weights | JEEK · Simulated graphs with known hubs | AUC | Helped |
| Use JEEK's entry-wise parallel optimization | JEEK · Joint sGGM estimation | time complexity: O(K^4p^5) -> O(K^4p^2) | Helped |
| Replace distance-weighted JEEK with JEEK-NK (no additional knowledge) (ablation) | JEEK · Simulated brain connectivity | F1-score | Hurt |
| Replace knowledge-weighted JEEK with JEEK-NK (ablation) | JEEK · ABIDE autism vs control | classification accuracy | Hurt |
| Vary the knowledge weight matrix (ablation) | JEEK · ABIDE autism vs control | classification accuracy | No clear effect |
| Run NAK for multi-task structure learning | NAK · Simulated brain connectivity | F1-score | Hurt |
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