Principal Component Analysis (PCA)

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Approximating a face using the first 10 eigenfaces Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 30
Approximating a face using the first 20 eigenfaces Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 31
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 and 20: Application: PCA on face images •
Page 21 and 22: The mean face Jochen Triesch, UC Sa
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: The mean face Jochen Triesch, UC Sa
Page 33 and 34: Approximating a face using all 97 e
Page 35: Eigenfaces in 3D • Blanz and Vett

Principal Component Analysis (PCA)

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