Title: Unsupervised prototype learning in an associative-memory network,
Abstract: Unsupervised learning in a generalized Hopfield associative-memory network is
investigated in this work. First, we prove that the (generalized) Hopfield
model is equivalent to a semi-restricted Boltzmann machine with a layer of
visible neurons and another layer of hidden binary neurons, so it could serve
as the building block for a multilayered deep-learning system. We then
demonstrate that the Hopfield network can learn to form a faithful internal
representation of the observed samples, with the learned memory patterns being
prototypes of the input data. Furthermore, we propose a spectral method to
extract a small set of concepts (idealized prototypes) as the most concise
summary or abstraction of the empirical data.