Research map / Learning theory and algorithms / Matrix and tensor methods
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use truncated-inner-product robust covariance estimator inside CLIME (Robust CLIME) instead of standard covariance | sparse precision matrix estimation · synthetic band/cluster graphs, arbitrary corruption, d=100 and 400 | spectral norm estimation error | Helped |
| Robust CLIME vs. outlier-robust baselines under outlier model | sparse precision matrix estimation · synthetic outlier model, d=400 | spectral norm estimation error: 2.877 vs 2.939 for tGLasso (band); 8.160 vs 8.586 (cluster) | Helped |
| Robust CLIME as the estimator in arbitrary corruption setting with corruption rate 0.1/0.2 | edge testing / confidence intervals · synthetic, outlier and arbitrary corruption | empirical type I error and CI coverage | No clear effect |
| Robust CLIME with n2 about 10-20 corruptions on real Arabidopsis gene data | gene network estimation · Arabidopsis gene expression (n=118, 39 genes) | qualitative network agreement | Mixed |
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