The present work proposes hybridization of Expectation-Maximization (EM) and KMeans techniques as an attempt to speed-up the clustering process. Though both K-Means and EM techniques look into different areas, K-means can be viewed as an approximate way to obtain maximum likelihood estimates for the means. Along with the proposed algorithm for hybridization, the present work also experiments wi...

In this work, a probabilistic model is established for recurrent networks. The expectation-maximization (EM) algorithm is then applied to derive a new fast training algorithm for recurrent networks through mean-field approximation. This new algorithm converts training a complicated recurrent network into training an array of individual feedforward neurons. These neurons are then trained via a l...

More sophisticated fuzzy clustering algorithms, like the Gustafson–Kessel algorithm [11] and the fuzzy maximum likelihood estimation (FMLE) algorithm [10] offer the possibility of inducing clusters of ellipsoidal shape and different sizes. The same holds for the expectation maximization (EM) algorithm for a mixture of Gaussians. However, these additional degrees of freedom can reduce the robust...

Background: In this study, Quantitative 32P bremsstrahlung planar and SPECT imaging and consequent dose assessment were carried out as a comprehensive phantom study to define an appropriate method for accurate Dosimetry in clinical practice. Materials and Methods: CT, planar and SPECT bremsstrahlung images of Jaszczak phantom containing a known activity of 32P were acquired. In addition, Phanto...

The speed of convergence of the Expectation Maximization (EM) algorithm for Gaussian mixture model fitting is known to be dependent on the amount of overlap among the mixture components. In this paper, we study the impact of mixing coefficients on the convergence of EM. We show that when the mixture components exhibit some overlap, the convergence of EM becomes slower as the dynamic range among...

Expectation Maximization (EM) is an efficient mixture-model based clustering method. In this paper, authors made an attempt to scale-up the algorithm, by reducing the computation time required for computing quadratic term, without sacrificing the accuracy. Probability density function (pdf) is to be calculated in EM, which involves evaluating quadratic term calculation. Three recursive approach...

We present an expectation-maximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have a normal EM algorithm. Compared to other mixture model approaches to self-organizing maps, the function our algorithm maximizes has a clear interpretation: it sums data log-likelihood and a pen...

This paper presents a new approach for the probability density function estimation using the Support Vector Machines (SVM) and the Expectation Maximization (EM) algorithms. In the proposed approach, an advanced algorithm for the SVM density estimation which incorporates the Mean Field theory in the learning process is used. Instead of using ad-hoc values for the parameters of the kernel functio...

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...

In this paper, we derive a new class of finite-dimensional filters for integrals and stochastic integrals of moments of the state for continuous-time linear Gaussian systems. Apart from being of significant mathematical interest, these new filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the model parameters.