Data Science, Machine Learning, Pca, Big Data, Principal Component.
cmap=’binary_r’) ax[1, i].imshow(projected[i].reshape(62, 47), cmap=’binary_r’) ax[0, 0].set_ylabel(‘complete\nresolution’) ax[1, 0].set_ylabel(‘150-D\nprojections’); As we can see the principal features extracted using PCA capture most of the variance in the dataset and thus, the projections