In the paper, justification is given for convergence of median modification classical expectation-maximization (EM) algorithm separation finite mixtures normal distributions. This designed to overcome instability EM with respect initial data.

This paper explores the e↵ects of parameter sharing on Bayesian network (BN) parameter learning when there is incomplete data. Using the Expectation Maximization (EM) algorithm, we investigate how varying degrees of parameter sharing, varying number of hidden nodes, and di↵erent dataset sizes impact EM performance. The specific metrics of EM performance examined are: likelihood, error, and the ...

We consider a space time coding system. We propose to detect symbols of the each user and estimate the channel iteratively. The channel gets estimated blindly via Expectation Maximization (EM) algorithm by formulating the problem as Gaussian mixture model (GMM). The estimated channel is then used to detect the symbols for each user, which is also done in an iterative fashion, i.e., user-wise de...

This research suggests a method for query expansion on Arabic Information Retrieval using Expectation Maximization (EM). We employ the EM algorithm in the process of selecting relevant terms for expanding the query and weeding out the non-related terms. We tested our algorithm on INFILE test collection of CLLEF2009, and the experiments show that query expansion that considers similarity of term...

In this paper the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation of a random vector is applied to the problem of symbol detection for CPM signals transmitted over timeselective Ftayleigh fading channels. This results in a soft-in soft-out (SISO) detection algorithm suitable for iterative detection/decoding schemes. Simulation results show that the error perfor...

We present a mixture model that can be applied to the recognition of multiple objects in an image plane. The model consists of any shape of submodules. Each submodule is a probability density function of data points with scale and shift parameters, and the modules are combined with weight probabilities. We present the EM (Expectation-Maximization) algorithm to estimate those parameters. We also...

In this paper we introduce a new algorithm for the estimation of source location parameters from array data given prior distributions on unknown nuisance source signal parameters. The conditional maximum-likelihood (CML) formulation is employed, and ML estimation is obtained by marginalizing over the nuisance parameters. In general, direct solution of this marginalization ML problem is intracta...

We introduce a probabilistic model that combines a classifier with an extra reinforcement signal (RS) encoding the probability of an erroneous feedback being delivered by the classifier. This representation computes the class probabilities given the task related features and the reinforcement signal. Using expectation maximization (EM) to estimate the parameter values under such a model shows t...

The Expectation-Maximization (EM) iterative algorithm is a broadly applicable statistical technique for maximizing complex likelihoods and handling the incomplete data problem. At each iteration step of the algorithm, two steps are performed: (i) E-Step consisting of projecting an appropriate functional containing the augmented data on the space of the original, incomplete data, and (ii) M-Step...