Example 1 • Use the data set "noisy. mat" available on your CD. The data set consists of 1965, 20 -pixel-by-28 -pixel grey-scale images distorted by adding Gaussian noises to each pixel with s=25.
Example 1 • Apply PCA to the noisy data. Suppose the intrinsic dimensionality of the data is 10. Compute reconstructed images using the top d = 10 eigenvectors and plot original and reconstructed images
Example 1 • If original images are stored in matrix X (it is 560 by 1965 matrix) and reconstructed images are in matrix X_hat , you can type in • colormap gray and then • imagesc(reshape(X(: , 10), 20 28)’) • imagesc(reshape(X_hat(: , 10), 20 28)’) to plot the 10 th original image and its reconstruction.
Example 2
Example 2 • Load the sample data, which includes digits 2 and 3 of 64 measurements on a sample of 400. load 2_3. mat • Extract appropriate features by PCA [u s v]=svd(X', 'econ'); • Create data Low_dimensional_data=u(: , 1: 2); • Observe low dimensional data Imagesc(Low_dimensional_data)