This note represents my attempt at explaining the EM algorithm (Hartley, 1958; Dempster et al., 1977; McLachlan and Krishnan, 1997). This is just a slight variation on TomMinka’s tutorial (Minka, 1998), perhaps a little easier (or perhaps not). It includes a graphical example to provide some intuition. 1 Intuitive Explanation of EM EM is an iterative optimizationmethod to estimate some unknown ...

We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood learning of Bayesian networks with belief propagation algorithms for approximate inference. Specifically we propose to combine the outer-loop step of convergent belief propagation algorithms with the M-step of the EM algorithm. This then yields an approximate EM algorithm that is essentially still d...

Technical reports from the Automatic Control group in Linkk oping are available by anonymous ftp at the address ftp.control.isy.liu.se. This report is contained in the compressed postscript le 2067.ps.Z.

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 investigate a new, fast and provably convergent MAP reconstruction algorithm for emission tomography. The new algorithm, termed C-OSEM has its origin in the alternating algorithm derivation of the well known EM algorithm for emission tomography. In this re-derivation, the complete data explicitly enters the objective function as an unknown variable. While the entire complete data gets update...

Keywords: Cluster analysis Maximum entropy principle k-means Fuzzy c-means Sample weights Robustness a b s t r a c t Although there have been many researches on cluster analysis considering feature (or variable) weights, little effort has been made regarding sample weights in clustering. In practice, not every sample in a data set has the same importance in cluster analysis. Therefore, it is in...