  Generative processes in biology and other fields often produce data that can
be regarded as resulting from a composition of basic features. Here we present
an unsupervised method based on autoencoders for inferring these basic features
of data. The main novelty in our approach is that the training is based on the
optimization of the `local entropy' rather than the standard loss, resulting in
a more robust inference, and enhancing the performance on this type of data
considerably. Algorithmically, this is realized by training an interacting
system of replicated autoencoders. We apply this method to synthetic and
protein sequence data, and show that it is able to infer a hidden
representation that correlates well with the underlying generative process,
without requiring any prior knowledge.

Quantitative Methods;Machine Learning;Machine Learning