The celebrated expectation-maximization (EM) algorithm is one of the most widely used optimization methods in statistics. In recent years it has been realized that EM algorithm is a special case of the more general minorization-maximization (MM) principle. Both algorithms creates a surrogate function in the first (E or M) step that is maximized in the second M step. This two step process always...

| We investigate multiuser signal detection with a base-station antenna array for synchronous CDMA Rayleigh fading uplink channels. We have developed a discrete-time model that enables the formulation of a spatial-temporal decorrelating detector using the maximum-likelihood criterion. The detector is shown to be near-far resistant. We further propose to implement the spatial-temporal decorrelat...

3 The Expectation-Maximization algorithm 7 3.1 Jointly-non-concave incomplete log-likelihood . . . . . . . . . . . 7 3.2 (Possibly) Concave complete data log-likelihood . . . . . . . . . . 8 3.3 The general EM derivation . . . . . . . . . . . . . . . . . . . . . 10 3.4 The E& M-steps . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.5 The EM algorithm . . . . . . . . . . . . . . . . . . ...

3 The Expectation-Maximization algorithm 7 3.1 Jointly-non-concave incomplete log-likelihood . . . . . . . . . . . 7 3.2 (Possibly) Concave complete data log-likelihood . . . . . . . . . . 8 3.3 The general EM derivation . . . . . . . . . . . . . . . . . . . . . 10 3.4 The E& M-steps . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.5 The EM algorithm . . . . . . . . . . . . . . . . . . ...

texture image analysis is one of the most important working realms of imageprocessing in medical sciences and industry. up to present, different approacheshave been proposed for segmentation of texture images. in this paper, we offeredunsupervised texture image segmentation based on markov random field (mrf)model. first, we used gabor filter with different parameters’ (frequency,orientation) va...

4 The Expectation-Maximization algorithm 7 4.1 Jointly-non-concave incomplete log-likelihood . . . . . . . . . . . 7 4.2 (Possibly) Concave complete data log-likelihood . . . . . . . . . . 8 4.3 The general EM derivation . . . . . . . . . . . . . . . . . . . . . 9 4.4 The E& M-steps . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.5 The EM algorithm . . . . . . . . . . . . . . . . . . ....

In this correspondence, we compare the expectation maximization (EM) algorithm with another iterative approach, namely, the iterative conditional estimation (ICE) algorithm, which was formally introduced in the field of statistical segmentation of images. We show that in case the probability density function (PDF) belongs to the exponential family, the EM algorithm is one particular case of the...

A novel maximum likelihood solution to the problem of identifying parameters of a nonlinear model under missing observations is presented. An expectation maximization (EM) algorithm, which uses the expected value of the complete log-likelihood function including the missing observations, is developed. The expected value of the complete log-likelihood (E-step) in the EM algorithm is approximated...

We present a general framework for unsupervised and semi-supervised learning containing a graded spectrum of Expectation Maximization (EM) algorithms. We call our framework Unified Expectation Maximization (UEM.) UEM allows us to tune the entropy of the inferred posterior distribution during the E-step to impact the quality of learning. Furthermore, UEM covers existing algorithms like standard ...