Research map / Learning theory and algorithms / Matrix and tensor methods
Matrix Variate Gaussian Mixture Distribution Steered Robust Metric Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Gaussian mixture block regularizer instead of capped norm regularizer | LMNN Cap · FGNET Aging | accuracy (%): 57.817 -> 59.812 | Helped |
| Gaussian mixture block regularizer instead of capped norm | LMNN Cap · VI and NIR Face | accuracy (%): 73.731 -> 74.428 | Helped |
| Gaussian mixture block regularizer instead of capped norm | LMNN Cap · OSR | accuracy (%): 76.511 -> 77.493 | Helped |
| Gaussian mixture block regularizer instead of capped norm | LMNN Cap · PubFig | accuracy (%): 87.125 -> 87.883 | Helped |
| add GMD regularizer to LMNN | LMNN · FGNET Aging | accuracy (%): 47.653 -> 59.812 | Helped |
| add GMD regularizer to LMNN, compared with SVM | SVM · VI and NIR Face | accuracy (%): 68.850 -> 74.428 | Helped |
| Gaussian mixture block regularizer instead of capped norm | LMNN Cap · PubFig face verification (ROC score) | ROC score: 0.845 -> 0.848 | Helped |
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- Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures (NeurIPS 2024 · similarity 0.82)
- RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees (NeurIPS 2025 · similarity 0.81)
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