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 su pervised learning

8. Problem statement

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

10. 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)

18. Class-Equivalence (Glasses Distinction)

20. Closed-form solution

24. Alternative constraints

28. Fisher Linear Discriminant Analysis (FDA)

30. Experimental results

38. Run-time comparison