We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM) algorithm. The NEM algorithm uses noise to speed up the convergence of the EM algorithm. The NEM theorem shows that additive noise speeds up the average convergence of the EM algorithm to a local maximum of the likelihood surface if a positivity condition holds. Corollary...

Parameter estimation in dynamic systems finds applications in various disciplines, including system biology. The well-known expectation-maximization (EM) algorithm is a popular method and has been widely used to solve system identification and parameter estimation problems. However, the conventional EM algorithm cannot exploit the sparsity. On the other hand, in gene regulatory network inferenc...

An Expectation-Maximization approach to sensor calibration is presented and applied to three-axis-magnetometer calibration. This approach is different from the existing attitude-independent approaches mainly in how the attitude parameters in the attitude sensor measurement model are handled. The attitude-independent approaches rely on a conversion of the body and reference representations of th...

We present a general framework containing a graded spectrum of Expectation Maximization (EM) algorithms called Unified Expectation Maximization (UEM.) UEM is parameterized by a single parameter and covers existing algorithms like standard EM and hard EM, constrained versions of EM such as ConstraintDriven Learning (Chang et al., 2007) and Posterior Regularization (Ganchev et al., 2010), along w...

We propose a Tabu search based Expectation Maximization (EM) algorithm for image segmentation in an unsupervised frame work. Hidden Markov Random Field (HMRF) model is used to model the images. The observed image is considered to be a realization of Gaussian Hidden Markov Random Field (GHMRF) model. The segmentation problem is formulated as a pixel labeling problem. The GHMRF model parameters a...

In this paper, an improved expectation maximization (EM) algorithm called statistical histogram based expectation maximization (SHEM) algorithm is presented. The algorithm is put forward to overcome the drawback of standard EM algorithm, which is extremely computationally expensive for calculating the maximum likelihood (ML) parameters in the statistical segmentation. Combining the SHEM algorit...

The approach proposed in this paper takes into account the uncertainty in colour modelling by employing variational Bayesian estimation. Mixtures of Gaussians are considered for modelling colour images. Distributions of parameters characterising colour regions are inferred from data statistics. The Variational Expectation-Maximization (VEM) algorithm is used for estimating the hyperparameters c...

In SPECT/PET, the maximum-likelihood expectation-maximization (ML-EM) algorithm is getting more attention as the speed of computers increases. This is because it can incorporate various physical aspects into the reconstruction process leading to a more accurate reconstruction than other analytical methods such as filtered-backprojection algorithms. However, the convergence rate of the ML-EM alg...

Performance of Language Identification (LID) System using Gaussian Mixture Models (GMM) is limited by the convergence of Expectation Maximization (EM) algorithm to local maxima. In this paper an LID system is described using Gaussian Mixture Models for the extracted features which are then trained using Split and Merge Expectation Maximization Algorithm that improves the global convergence of E...


 This paper discuss about the use face patteren recognition which is now days become popular especialy on smartphone lock screen system. The method used in this research are Expectation – Maximization (EM) Algorithm. EM Algorithm an iterative optimization for estimation of Maximum Likelihood (ML) incomplete data problems. there 2 stages, namely stage E (E-step) and M (M-step). These two s...