Research map / Learning theory and algorithms / Matrix and tensor methods
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed random-matrix-based gradient descent estimator instead of the sample covariance matrix | Covariance estimation · Synthetic covariance matrices | Fisher distance | Helped |
| Use the proposed estimator instead of competing methods | LDA and QDA · Synthetic scenarios and EEG data | classification accuracy | Helped |
| Approximate the Riemannian gradient-descent update with its second-order expansion (ablation) | Proposed covariance estimator · Synthetic covariance estimation | solution accuracy | No clear effect |
| Use the proposed method instead of QuEST1 or QuEST2 | Covariance and precision matrix estimation · Synthetic covariance matrices | Fisher distance | Mixed |
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