Principal Component Analysis (PCA)

More documents

Recommendations

Info

The original images (12 out of 97) Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 20
The mean face Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 21
Page 1 and 2: Principal Component Analysis (PCA)
Page 3 and 4: Example: • consider projecting x
Page 5 and 6: • continuing with some linear alg
Page 7 and 8: Notes: • if v is an eigenvector o
Page 9 and 10: What are the variances of the PCA p
Page 11 and 12: PCA for multinomial Gaussian • We
Page 13 and 14: Transformation matrix for PCA • L
Page 15 and 16: • PCA expresses the mean-subtract
Page 17 and 18: • Equivalent view: To compress n-
Page 19: Application: PCA on face images •
Page 23 and 24: The transpose trick The eigenvector
Page 25 and 26: Eigenfaces • Eigenfaces (the prin
Page 27 and 28: PCA for low-dimensional low dimensi
Page 29 and 30: The mean face Jochen Triesch, UC Sa
Page 31 and 32: Approximating a face using the firs
Page 33 and 34: Approximating a face using all 97 e
Page 35: Eigenfaces in 3D • Blanz and Vett

Principal Component Analysis (PCA)

Create successful ePaper yourself

Delete template?

Save as template?