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Distance metric learning Vs. Fisher discriminant analysis

Distance metric learning Vs. Fisher discriminant analysis. Different Features. Glasses vs. No Glasses. Beard vs. No Beard. Learning a Metric. Class-Equivalence Side information (Unsupervised Learning). Extension to su pervised learning. Problem statement.

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Distance metric learning Vs. Fisher discriminant analysis

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  1. Distance metric learning Vs. Fisher discriminant analysis

  2. Different Features

  3. Glasses vs. No Glasses

  4. Beard vs. No Beard

  5. Learning a Metric

  6. Class-Equivalence Side information (Unsupervised Learning)

  7. Extension to supervised learning

  8. Problem statement

  9. Distance metric learningby Eric P. Xing, Andrew Y. Ng, Michael I. Jordan and Stuart Russell

  10. Maximally Collapsing Metric Learning (MCML)

  11. Maximally Collapsing Metric Learning (MCML)

  12. Improving embeddings by flexible exploitation of side information.Ghodsi, A.; Wilkinson, D. F.; and Southey, F.

  13. Improving embeddings

  14. Useful Matrix Identities

  15. Improving embeddings

  16. Class-Equivalence (Beard Distinction)

  17. Class-Equivalence (Beard Distinction)

  18. Class-Equivalence (Glasses Distinction)

  19. Class-Equivalence (Glasses Distinction)

  20. Closed-form solution

  21. Closed-form solution

  22. Closed-form solution

  23. Closed-form solution

  24. Alternative constraints

  25. Alternative constraints

  26. Alternative constraints

  27. Alternative constraints

  28. Fisher Linear Discriminant Analysis (FDA)

  29. Fisher Linear Discriminant Analysis (FDA)

  30. Experimental results

  31. Experimental results

  32. Experimental results

  33. Experimental results

  34. Experimental results

  35. Experimental results

  36. Experimental results

  37. Experimental results

  38. Run-time comparison

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