Non-parametric Bayesian Learning with Deep Learning Structure and Its Applications in Wireless Networks
Machine Learning
2014-10-27 v2 Neural and Evolutionary Computing
Networking and Internet Architecture
Machine Learning
Abstract
In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can be potentially infinite. Moreover, the number of layers can also be infinite. We construct the model structure that allows continuous values for the hidden factors and weights, which makes the model suitable for various applications. We use the Metropolis-Hastings method to infer the model structure. Then the performance of the algorithm is evaluated by the experiments. Simulation results show that the model fits the underlying structure of simulated data.
Cite
@article{arxiv.1410.4599,
title = {Non-parametric Bayesian Learning with Deep Learning Structure and Its Applications in Wireless Networks},
author = {Erte Pan and Zhu Han},
journal= {arXiv preprint arXiv:1410.4599},
year = {2014}
}
Comments
5 pages, 2 figures and 1 algorithm list