the reconstructions of the original data using only some components in figure - after dropping the second component and arriving at the third panelwe undid the rotation and added the mean back to obtain new points in the original space with the second component removedas shown in the last panel we can do similar transformation for the faces by reducing the data to only some principal components and then rotating back into the original space this return to the original feature space can be done using the inverse_transform method herewe visualize the reconstruction of some faces using or , components (figure - )in[ ]mglearn plots plot_pca_faces(x_trainx_testimage_shapefigure - reconstructing three face images using increasing numbers of principal components you can see that when we use only the first principal componentsonly the essence of the picturelike the face orientation and lightingis captured by using more and more principal componentsmore and more details in the image are preserved this unsupervised learning and preprocessing