Data Science, Machine Learning, Pca, Big Data, Principal Component.
faces_pca.transform(faces.data) projected = faces_pca.inverse_transform(components) # Plot the results fig, ax = plt.subplots(2, 15, figsize=(15, 2.5), subplot_kw={‘xticks’:[], ‘yticks’:[]}, gridspec_kw=dict(hspace=0.1, wspace=0.1)) for i in range(15): ax[0, i].imshow(faces.data[i].reshape(62, 47),