The EM algorithm of Dempster, Laird, and Rubin (1977) is a broadly applicable approach that has been widely applied to the iterative computation of maximum likelihood estimates in a variety of incomplete-data problems. A criticism that has been levelled at the EM algorithm is that its convergence can be quite slow. Unfortunately, methods to accelerate the EM algorithm do tend to sacriice the si...

The Expectation-Maximization (EM) algorithm is a general technique for maximum likelihood estimation (MLE). In this paper we present several of the important theoretical and practical issues associated with Gaussian mixture mod-eling (GMM) within the EM framework. First, we propose an EM algorithm for estimating the parameters of a special GMM structure, named a probablistic mapping network (PM...

y In this paper, a space-alternating generalized expectation-maximization (SAGE) algorithm is presented for the numerical computation of maximum-likelihood (ML) and penalized maximum-likelihood (PML) estimates of the parameters of covariance matrices with linear structure for complex Gaussian processes. By using a less informative hidden-data space and a sequential parameter-update scheme, a SA...

This EM review article focuses on parameter expansion, a simple technique introduced in the PX-EM algorithm to make EM converge faster while maintaining its simplicity and stability. The primary objective concerns the connection between parameter expansion and efficient inference. It reviews the statistical interpretation of the PX-EM algorithm, in terms of efficient inference via bias reductio...

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery is guaranteed to converge, but it converges slowly. Its ordered-subset version (OS-EM) is used widely in tomographic image reconstruction because of its order-of-magnitude acceleration compared with the EM algorithm, but it does not guarantee convergence. Recently the ordered-subset, separable-paraboloidal-surroga...

Finite mixture models implemented via the EM algorithm are being increasingly used in a wide range of problems in the context of unsupervised statistical pattern recognition. As each E-step visits each feature vector on a given iteration, the EM algorithm requires considerable computation time in its application to large data sets. We consider two approaches, an incremental EM (IEM) algorithm a...

Based on the electromagnetism-like algorithm (EM), we propose a novel hybrid learning algorithms which is the improved EM algorithm with genetic algorithm technique (IEMGA) for recurrent fuzzy neural system design. IEMGA are composed of initialization, local search, total force calculation, movement, and evaluation. They are hybridization of EM and GA. EM algorithm is a population-based meta-he...

We consider regularization methods of Kaczmarz type in connection with the expectation-maximization (EM) algorithm for solving ill-posed equations. For noisy data, our methods are stabilized extensions of the well established ordered-subsets expectation-maximization iteration (OS-EM). We show monotonicity properties of the methods and present a numerical experiment which indicates that the exte...

This paper discusses EM algorithm and Bootstrap approach combination applied for the improvement of the satellite image fusion process. This novel satellite image fusion method based on estimation theory EM algorithm and reinforced by Bootstrap approach was successfully implemented and tested. The sensor images are firstly split by a Bayesian segmentation method to determine a joint region map ...

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...