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arXiv · 1512.00927

Mean-Field Inference in Gaussian Restricted Boltzmann Machine

Chako Takahashi·Muneki Yasuda
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Abstract

A Gaussian restricted Boltzmann machine (GRBM) is a Boltzmann machine defined on a bipartite graph and is an extension of usual restricted Boltzmann machines. A GRBM consists of two different layers: a visible layer composed of continuous visible variables and a hidden layer composed of discrete hidden variables. In this paper, we derive two different inference algorithms for GRBMs based on the naive mean-field approximation (NMFA). One is an inference algorithm for whole variables in a GRBM, and the other is an inference algorithm for partial variables in a GBRBM. We compare the two methods analytically and numerically and show that the latter method is better.

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Chako Takahashi, Muneki Yasuda. 2016-03-18. Mean-Field Inference in Gaussian Restricted Boltzmann Machine. https://doi.org/10.7566/jpsj.85.034001

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