Deep learning via semi-supervised embedding

Abstract

We show how nonlinear semi-supervised embedding algorithms popular for use with "shallow" learning techniques such as kernel methods can be easily applied to deep multi-layer architectures, either as a regularizer at the output layer, or on each layer of the architecture. Compared to standard supervised backpropagation this can give significant gains. This trick provides a simple alternative to existing approaches to semi-supervised deep learning whilst yielding competitive error rates compared to those methods, and existing shallow semi-supervised techniques. © Springer-Verlag Berlin Heidelberg 2012.