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Unsupervised prototype learning in an associative-memory network

Abstract · Apr 10, 2017 13:20 ·

neurons hopfield rbm visible memory associative neuron cs-ne cond-mat-dis-nn cs-lg

Arxiv Abstract

  • Huiling Zhen
  • Shang-Nan Wang
  • Hai-Jun Zhou

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.

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