The following code block demonstrates how each of the faces can be approximately represented as a linear combination of only those 64 dominant eigenfaces. The inverse_transform() function from scikit-learn is used to go back to the original space but only with these 64 dominant eigenfaces, discarding all other eigenfaces:
# face reconstructionfaces_inv_proj = pipeline.named_steps['pca'].inverse_transform(faces_proj) #reshaping as 400 images of 64x64 dimension fig = plt.figure(figsize=(5,5)) fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05) # plot the faces, each image is 64 by 64 dimension but 8x8 pixels j = 1np.random.seed(0)for i in np.random.choice(range(faces.shape[0]), 25):
ax = fig.add_subplot(5, ...
