The potential of the SAGE (Space Alternating Generalized Expectation Maximization) algorithm for navigation systems in order to distinguish the line-of-sight signal (LOSS) is to be considered. The SAGE algorithm is a low-complexity generalization of the EM (Expectation-Maximization) algorithm, which iteratively approximates the maximum likelihood estimator (MLE) and has been successfully applie...

The data mining community voted Expectation Maximization (EM) algorithm as one of the top ten algorithms having the most impact on data mining research [5]. EM is a popular iterative algorithm for learning mixture models with applications in various areas from computer vision, astronomy, to signal processing. We present a new high-performance parallel algorithm on multicore systems that impacts...

A well studied procedure for estimating a parameter from observed data is to maximize the likelihood function. When a maximizer cannot be obtained in closed form, iterative maximization algorithms, such as the expectation maximization (EM) maximum likelihood algorithms, are needed. The standard formulation of the EM algorithms postulates that finding a maximizer of the likelihood is complicated...

The current paper addresses the issue of estimating the sampling instant in turbo receivers. The proposed synchronizer is based on the expectation-maximization (EM) algorithm and takes benefit from the soft information delivered by the turbo system. Performance Interpolator Anti−aliasing Turbo Demodulator Matched filter a posteriori information Discrete Time 1 Ts rl r(t)

In this work, we provide a theoretical framework that unifies the notions of hidden representations and moving targets through the expectation and maximization (EM) algorithm. Based on such a framework, two fast training algorithms can be derived consistently for both feedforward networks and recurrent networks.

Practical applications of the theory of knowledge structures often rely on a probabilistic version, known as the basic local independence model. The paper outlines various procedures for estimating its parameters, including maximum likelihood (ML) via the expectation-maximization (EM) algorithm, the computationally efficient minimum discrepancy (MD) estimation as well as MDML, a hybrid method c...

This paper focuses on a key aspect of Statistical Machine Translation: word alignment. Various word alignment models are presented, first differentiating between methods and then highlighting the preferred method. A partially detailed mathematical explanation is provided for each model as well as a brief implementation of the Expectation Maximization Algorithm (EM Algorithm) for later models. F...

In this article, we explore the theoretical aspects of the expectation maximization algorithm and how it can be applied to estimation of data as a Gaussian mixture model. We form comparisons and show how by the simplification of some parameters we can form the heuristic k-means algorithm. We then demonstrate through the authors’ code several situations where the EM-GMM algorithm performs signif...

We propose an expectation maximization (EM)based algorithm for semi-blind channel estimation of reciprocal channels in amplify-and-forward (AF) two-way relay networks (TWRNs). By incorporating both data samples and pilots into the estimation, the proposed algorithm provides substantially higher accuracy than the conventional training-based approach. Furthermore, the proposed algorithm has a lin...

The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize applicatio...