  Informed by the basic geometry underlying feed forward neural networks, we
initialize the weights of the first layer of a neural network using the linear
discriminants which best distinguish individual classes. Networks initialized
in this way take fewer training steps to reach the same level of training, and
asymptotically have higher accuracy on training data.
