Learning Boltzmann Machine with EM-like Method
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.
Cite
@article{arxiv.1609.01840,
title = {Learning Boltzmann Machine with EM-like Method},
author = {Jinmeng Song and Chun Yuan},
journal= {arXiv preprint arXiv:1609.01840},
year = {2016}
}