Non-parametric Bayesian Learning with Deep Learning Structure and Its Applications in Wireless Networks
This work addresses data analysis challenges in wireless networks, but it appears incremental as it combines existing non-parametric Bayesian and deep learning concepts without clear novel breakthroughs.
The authors tackled the problem of extracting hidden factors from observed data with unknown and potentially infinite numbers of factors and layers, using a non-parametric Bayesian model, and simulation results demonstrated that the model fits the underlying structure of simulated data.
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.