The Electromagnetism-like (EM) algorithm is a populationbased stochastic global optimization algorithm that uses an attractionrepulsion mechanism to move sample points towards the optimal. In this paper, an implementation of the EM algorithm in the Matlab environment as a useful function for practitioners and for those who want to experiment a new global optimization solver is proposed. A set o...

Statistical iterative methods for image reconstruction like maximum likelihood expectation maximization (ML-EM) are more robust and flexible than analytical inversion methods and allow for accurately modeling the counting statistics and the photon transport during acquisition. They are rapidly becoming the standard for image reconstruction in emission computed tomography. The maximum likelihood...

The Expectation-Maximization (EM) algorithm is a very general and popular iterative computational algorithm to find maximum likelihood estimates from incomplete data and broadly used to statistical analysis with missing data, because of its stability, flexibility and simplicity. However, it is often criticized that the convergence of the EM algorithm is slow. The various algorithms to accelerat...

Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algorithms. In order to limit the search space and escape from local maxima produce...

We address the task of reconstructing images corrupted by Poisson noise, which is important in various applications, such as fluorescence microscopy, positron emission tomography (PET), or astronomical imaging. In this work, we focus on reconstruction strategies, combining the expectation-maximization (EM) algorithm and total variation (TV) based regularization, and present a detailed analysis ...

This paper presents the use of the feasibility and dominance (FAD) rules in a electromagnetism-like mechanism (EM) for the constraints handling in constrained global optimization. The FAD rules are easily incorporated in the EM algorithm: when two points are compared at a time and a selection is required, and when the direction of individual forces between two points has to be decided. Numerica...

We show a close relationship between the Expectation Maximization (EM) algorithm and direct optimization algorithms such as gradientbased methods for parameter learning. We identify analytic conditions under which EM exhibits Newton-like behavior, and conditions under which it possesses poor, first-order convergence. Based on this analysis, we propose two novel algorithms for maximum likelihood...

We consider the problem of joint carrier frequency offset and channel estimation between transmitter and receiver in a frequency-selective channel MIMO-OFDM system. Recently two high performance estimators based on the expectation-maximization (EM) algorithm have been proposed. The main drawback of the maximum likelihood base algorithms, like EM algorithm, is the high computational complexity. ...

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, con...

Multi-user detection (MUD) is one standard of 3G, which can effectively reduce the multiple access interference (MAI) and increase the system capacity. The Expectation-Maximization (EM) iterative algorithm is commonly used in recent years for missing data, which could be applied to MUD system. But the EM algorithm has a fatal weakness that its slow convergence speed. The new accelerated EM Algo...