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arXiv · 1609.01840

Learning Boltzmann Machine with EM-like Method

Jinmeng Song·Chun Yuan
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Abstract

We propose an expectation-maximization-like(EMlike) method to train Boltzmann machine with unconstrained connectivity. It adopts Monte Carlo approximation in the E-step, and replaces the intractable likelihood objective with efficiently computed objectives or directly approximates the gradient of likelihood objective in the M-step. The EM-like method is a modification of alternating minimization. We prove that EM-like method will be the exactly same with contrastive divergence in restricted Boltzmann machine if the M-step of this method adopts special approximation. We also propose a new measure to assess the performance of Boltzmann machine as generative models of data, and its computational complexity is O(Rmn). Finally, we demonstrate the performance of EM-like method using numerical experiments.

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Jinmeng Song, Chun Yuan. 2016-09-07. Learning Boltzmann Machine with EM-like Method. https://arxiv.org/abs/1609.01840

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