Do the 30-D codes found by the deep
autoencoder preserve the class
structure of the data?
Take the 30-D activity patterns in the code layer
and display them in 2-D using a new form of
non-linear multi-dimensional scaling
The method is called UNI-SNE (Cook et. al.
It keeps similar patterns close together and
tries to push dissimilar ones far apart.
Does the learning find the natural classes?