Wael Abd-Almageed

Estimating time-varying densities using a stochastic learning automaton

TitleEstimating time-varying densities using a stochastic learning automaton
Publication TypeJournal Article
Year of Publication2005
AuthorsW. Abd-Almageed, A. I. El-Osery, and C. E. Smith
JournalSoft Computing
Pages1007–1020
Date Publishednov
Abstract

The popular Expectation Maximization technique suffers a major drawback when used to approximate a density function using a mixture of Gaussian components; that is the number of components has to be a priori specified. Also, Expectation Maximization by itself cannot estimate time-varying density functions. In this paper, a novel stochastic technique is introduced to overcome these two limitations. Kernel density estimation is used to obtain a discrete estimate of the true density of the given data. A Stochastic Learning Automaton is then used to select the number of mixture components that minimizes the distance between the density function estimated using the Expectation Maximization and discrete estimate of the density. The validity of the proposed approach is verified using synthetic and real univariate and bivariate observation data.

URLhttp://link.springer.com/article/10.1007/s00500-005-0028-4
Groups: