Computerized tomography (CT) plays a critical role in modern medicine. However, the radiation associated with CT is significant. Methods that can enable CT imaging with less radiation exposure but without sacrificing image quality are therefore extremely important. This paper introduces a novel method for enabling image reconstruction at lower radiation exposure levels with convergence analysis...

In this paper the authors derive a new class of finite-dimensional recursive filters for linear dynamical systems. The Kalman filter is a special case of their general filter. Apart from being of mathematical interest, these new finite-dimensional filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the parameters of a linear dynamical sy...

Since histograms of many real network traces show strong evidence of mixture, this paper uses mixture distributions to model Internet traffic and applies the EM algorithm to fit the models. Making use of the fact that at each iteration of the EM algorithm the parameter increment has a positive projection on the gradient of the likelihood function, this paper proposes an online EM algorithm to f...

The EM algorithm can be used to estimate conditional probabilities for matching field patterns for the Fellegi-Sunter model for record linkage. The algorithm is based on a latent class model for the record pairs where one of the classes is the set of true matches. If the number of true match pairs in the data set is too small, then the EM algorithm cannot detect the correct latent class. We con...

We introduce a novel approach named unambiguity regularization for unsupervised learning of probabilistic natural language grammars. The approach is based on the observation that natural language is remarkably unambiguous in the sense that only a tiny portion of the large number of possible parses of a natural language sentence are syntactically valid. We incorporate an inductive bias into gram...

We present a new multiple-instance (MI) learning technique (EMDD) that combines EM with the diverse density (DD) algorithm. EM-DD is a general-purpose MI algorithm that can be applied with boolean or real-value labels and makes real-value predictions. On the boolean Musk benchmarks, the EM-DD algorithm without any tuning significantly outperforms all previous algorithms. EM-DD is relatively ins...

An iterative fuzzy clustering method is proposed to partition a set of multivariate binary observation vectors located at neighboring geographic sites. The method described here applies in a binary setup a recently proposed algorithm, called Neighborhood EM, which seeks a a partition that is both well clustered in the feature space and spatially regular [2]. This approach is derived from the EM...

Two major reasons for the popularity of the EM algorithm are that its maximum step involves only complete-data maximum likelihood estimation, which is often computationally simple, and that its convergence is stable, with each iteration increasing the likelihood. When the associated complete-data maximum likelihood estimation itself is complicated, EM is less attractive because the M-step is co...

This paper is about the estimation of fixed model parameters in hidden Markov models using an online (or recursive) version of the Expectation-Maximization (EM) algorithm. It is first shown that under suitable mixing assumptions, the large sample behavior of the traditional (batch) EM algorithm may be analyzed through the notion of a limiting EM recursion, which is deterministic. This observati...

Maximum Likelihood estimation based Expectation Maximization(EM) reconstruction algorithm [ 11 has been shown to provide good quality reconstruction for PET. Our previous work [2,3] introduced multigrid concept for PET image reconstruction using EM. The multiresolution EM (MREM) algorithm is an attempt to improve the EM based estimation through an effective use of multi-resolution grids in both...