Dimensionality Reduction with Spark - Machine Learning with Spark - page 314

Note
This function is adapted from the LFW dataset example code in the scikit-learn document-
ation available at http://scikit-learn.org/stable/auto_examples/applications/
face_recognition.html .
We will now use this function to plot the top 10 Eigenfaces:
plot_gallery(pcs, 50, 50)
This should display the following plot:
Top 10 Eigenfaces
Interpreting the Eigenfaces
Looking at the preceding images, we can see that the PCA model has effectively extracted
recurring patterns of variation, which represent various features of the facial images. Each
principal component can, as with clustering models, be interpreted. Again, like clustering,
it is not always straightforward to interpret precisely what each principal component rep-
resents.