Main content

Comparing Approaches to Initializing the Expectation-Maximization Algorithm

Show full item record

Title: Comparing Approaches to Initializing the Expectation-Maximization Algorithm
Author: Dicintio, Sabrina
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: McNicholas, Paul
Abstract: The expectation-maximization (EM) algorithm is a widely utilized approach to max- imum likelihood estimation in the presence of missing data, this thesis focuses on its application within the model-based clustering framework. The performance of the EM algorithm can be highly dependent on how the algorithm is initialized. Several ways of initializing the EM algorithm have been proposed, however, the best method to use for initialization remains a somewhat controversial topic. From an attempt to obtain a superior method of initializing the EM algorithm, comes the concept of using multiple existing methods together in what will be called a `voting' procedure. This procedure will use several common initialization methods to cluster the data, then a nal starting ^zig matrix will be obtained in two ways. The hard `voting' method follows a majority rule, whereas the soft `voting' method takes an average of the multiple group memberships. The nal ^zig matrix obtained from both methods will dictate the starting values of ^ g; ^ g; and ^ g used to initialize the EM algorithm.
Date: 2012-08

Files in this item

Files Size Format View
Dicintio_Sabrina_201210_Msc.pdf 428.7Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record