Composite Likelihood Estimation for Restricted Boltzmann machines
Machine Learning
2014-06-25 v1
Abstract
Learning the parameters of graphical models using the maximum likelihood estimation is generally hard which requires an approximation. Maximum composite likelihood estimations are statistical approximations of the maximum likelihood estimation which are higher-order generalizations of the maximum pseudo-likelihood estimation. In this paper, we propose a composite likelihood method and investigate its property. Furthermore, we apply our composite likelihood method to restricted Boltzmann machines.
Cite
@article{arxiv.1406.6176,
title = {Composite Likelihood Estimation for Restricted Boltzmann machines},
author = {Muneki Yasuda and Shun Kataoka and Yuji Waizumi and Kazuyuki Tanaka},
journal= {arXiv preprint arXiv:1406.6176},
year = {2014}
}