Research map / Learning theory and algorithms / Combinatorial and search optimization
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use convex CSFN regularization in MDML instead of nonconvex SFN regularization in PDML | Orthogonality-promoting distance metric learning · MIMIC | AUC (A-All): 0.662 -> 0.668 | Helped |
| Use convex CVND regularization in MDML instead of nonconvex VND regularization in PDML | Orthogonality-promoting distance metric learning · MIMIC | AUC (A-All): 0.667 -> 0.672 | Helped |
| Use convex CLDD regularization in MDML instead of nonconvex LDD regularization in PDML | Orthogonality-promoting distance metric learning · MIMIC | AUC (A-All): 0.664 -> 0.669 | Helped |
| Use convex CSFN regularization in MDML instead of nonconvex SFN regularization in PDML | Orthogonality-promoting distance metric learning · Reuters | AUC (A-All): 0.973 -> 0.978 | Helped |
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