Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Hi-index | 754.84 |
Accelerated algorithms for maximum-likelihood image reconstruction are essential for emerging applications such as three-dimensional (3-D) tomography, dynamic tomographic imaging, and other high-dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum-likelihood estimates and study its convergence properties. These methods are based on a proximal point algorithm implemented with the Kullback-Liebler (KL) divergence between posterior densities of the complete data as a proximal penalty function. When the proximal relaxation parameter is set to unity, one obtains the classical expectation-maximization (EM) algorithm. For a decreasing sequence of relaxation parameters, relaxed versions of EM are obtained which can have much faster asymptotic convergence without sacrifice of monotonicity. We present an implementation of the algorithm using More's (1983) trust region update strategy. For illustration, the method is applied to a nonquadratic inverse problem with Poisson distributed data