Mean-Field Inference in Gaussian Restricted Boltzmann Machine
Machine Learning
2016-03-21 v2 Data Analysis, Statistics and Probability
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.
Cite
@article{arxiv.1512.00927,
title = {Mean-Field Inference in Gaussian Restricted Boltzmann Machine},
author = {Chako Takahashi and Muneki Yasuda},
journal= {arXiv preprint arXiv:1512.00927},
year = {2016}
}