Title :
Non-parametric Bayesian learning with deep learning structure and its applications in wireless networks
Author :
Pan, Erte ; Zhu Han
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Houston, Houston, TX, USA
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.
Keywords :
belief networks; learning (artificial intelligence); radio networks; telecommunication computing; deep learning structure; infinite hierarchical nonparametric Bayesian model; metropolis-hastings method; nonparametric Bayesian learning; performance evaluation; wireless networks; Bayes methods; Cognitive radio; Data models; Inference algorithms; Signal processing; Signal processing algorithms; Vectors; Indian Buffet Process; Metropolis-Hastings algorithm; deep learning; non-parametric Bayesian learning;
Conference_Titel :
Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on
Conference_Location :
Atlanta, GA
DOI :
10.1109/GlobalSIP.2014.7032319
