  Feature Learning aims to extract relevant information contained in data sets
in an automated fashion. It is driving force behind the current deep learning
trend, a set of methods that have had widespread empirical success. What is
lacking is a theoretical understanding of different feature learning schemes.
This work provides a theoretical framework for feature learning and then
characterizes when features can be learnt in an unsupervised fashion. We also
provide means to judge the quality of features via rate-distortion theory and
its generalizations.
