Bayesian Topology Learning and noise removal from network data

Abstract

Learning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph.

Research on Lung Ultrasound Image Classification Based on Compressed Sensing

“…Substituting ( 1 ) into ( 2 ), we can get where Θ is called the sensor matrix. Ramezani Mayiami et al have proved that in order to restore the observation signal without distortion, the sensor matrix needs to satisfy the restricted isometry property (RIP) [ 9 ]. When the sparse basis and the observation matrix are not correlated with each other, the sensing matrix can be made to satisfy the finite equidistance property.…”

Correction to: Bayesian Topology Learning and noise removal from network data

“…In the original publication [1] there were are 2 incorrect reference citations which had to be removed. In this correction article the incorrect and correct information is listed.…”