CS 479

1. CS 479/679Pattern Recognition – Spring 2006Dimensionality Reduction Using PCA/LDAChapter 3 (Duda et al.) – Section 3.8 Case Studies: Face Recognition Using Dimensionality Reduction M. Turk, A. Pentland, "Eigenfaces for Recognition", Journal of Cognitive Neuroscience, 3(1), pp. 71-86, 1991. D. Swets, J. Weng, "Using Discriminant Eigenfeatures for Image Retrieval", IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8), pp. 831-836, 1996. A. Martinez, A. Kak, "PCA versus LDA", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, 2001. Good afternoon and thank you everyone for coming. My talk today will describe the research I performed at the IRIS at USC, the object of this work being to build a computational framework that addresses the problem of motion analysis and interpretation.Good afternoon and thank you everyone for coming. My talk today will describe the research I performed at the IRIS at USC, the object of this work being to build a computational framework that addresses the problem of motion analysis and interpretation.

2. 2 Dimensionality Reduction One approach to deal with high dimensional data is by reducing their dimensionality. Project high dimensional data onto a lower dimensional sub-space using linear or non-linear transformations.

3. 3 Dimensionality Reduction Linear transformations are simple to compute and tractable. Classical –linear- approaches: Principal Component Analysis (PCA) Fisher Discriminant Analysis (FDA)

4. 4 Principal Component Analysis (PCA)

5. 5 Principal Component Analysis (PCA)

6. 6 Principal Component Analysis (PCA)

7. 7 Principal Component Analysis (PCA)

8. 8 Principal Component Analysis (PCA)

9. 9 Principal Component Analysis (PCA)

10. 10 Principal Component Analysis (PCA) Eigenvalue spectrum

11. 11 Principal Component Analysis (PCA)

12. 12 Principal Component Analysis (PCA)

13. 13 Principal Component Analysis (PCA)

14. 14 Principal Component Analysis (PCA)

15. 15 Principal Component Analysis (PCA)

16. 16 Principal Component Analysis (PCA)

17. 17 Principal Component Analysis (PCA)

18. 18 Principal Component Analysis (PCA)

19. 19 Principal Component Analysis (PCA)

20. 20 Principal Component Analysis (PCA)

21. 21 Principal Component Analysis (PCA)

22. 22 Principal Component Analysis (PCA)

23. 23 Principal Component Analysis (PCA) Eigenvalue spectrum

24. 24 Principal Component Analysis (PCA)

25. 25 Principal Component Analysis (PCA)

26. 26 Principal Component Analysis (PCA)

27. 27 Principal Component Analysis (PCA)

28. 28 Principal Component Analysis (PCA)

29. 29 Principal Component Analysis (PCA)

30. 30 Principal Component Analysis (PCA)

31. 31 Principal Component Analysis (PCA)

32. 32 Principal Component Analysis (PCA)

33. 33 Principal Component Analysis (PCA)

34. 34 Principal Component Analysis (PCA)

35. 35 Principal Component Analysis (PCA)

36. 36 Principal Component Analysis (PCA)

37. 37 Linear Discriminant Analysis (LDA)

38. 38 Linear Discriminant Analysis (LDA)

39. 39 Linear Discriminant Analysis (LDA)

40. 40 Linear Discriminant Analysis (LDA)

41. 41 Linear Discriminant Analysis (LDA)

42. 42 Linear Discriminant Analysis (LDA)

43. 43 Linear Discriminant Analysis (LDA)

44. 44 Linear Discriminant Analysis (LDA)

45. 45 Linear Discriminant Analysis (LDA)

46. 46 Linear Discriminant Analysis (LDA)

47. 47 Linear Discriminant Analysis (LDA)

48. 48 Linear Discriminant Analysis (LDA)

49. 49 Linear Discriminant Analysis (LDA)

50. 50 Linear Discriminant Analysis (LDA)

51. 51 Linear Discriminant Analysis (LDA)

52. 52 Linear Discriminant Analysis (LDA)

53. 53 Linear Discriminant Analysis (LDA)

54. 54 Linear Discriminant Analysis (LDA)

55. 55 Linear Discriminant Analysis (LDA)

56. 56 Linear Discriminant Analysis (LDA)