Accession Number : ADA295637

Title :   On Convergence Properties of the EM Algorithm for Gaussian Mixtures.

Descriptive Note : Memorandum rept.,

Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB

Personal Author(s) : Jordan, Michael ; Xu, Lei

PDF Url : ADA295637

Report Date : 17 JAN 1995

Pagination or Media Count : 11

Abstract : Expectation-Maximization(EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix $P$, and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of $P$ and provide new results analyzing the effect that $P$ has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models. (AN)

Descriptors :   *ALGORITHMS, *NEURAL NETS, *LEARNING, MATHEMATICAL MODELS, OPTIMIZATION, DATA MANAGEMENT, MAXIMUM LIKELIHOOD ESTIMATION, MATRICES(MATHEMATICS), PROBABILITY, CONVERGENCE, ITERATIONS, MARKOV PROCESSES.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE