  We consider regularization methods of Kaczmarz type in connection with the
expectation-maximization (EM) algorithm for solving ill-posed equations. For
noisy data, our methods are stabilized extensions of the well established
ordered-subsets expectation-maximization iteration (OS-EM). We show
monotonicity properties of the methods and present a numerical experiment which
indicates that the extended OS-EM methods we propose are much faster than the
standard EM algorithm.
