Expectation maximization and its application in modeling, segmentation and anomaly detection

Author

Ritesh Ganju

Abstract

"Expectation Maximization (EM) is a general purpose algorithm for solving maximum likelihood estimation problems in a wide variety of situations best described as incomplete data problems. The incompleteness of the data may arise due to missing data, truncated distributions, etc. One such case is a mixture model, where the class association of the data is unknown. In these models, the EM algorithm is used to estimate the parameters of parametric mixture distributions along with the probabilities of occurrence. In this thesis, the EM algorithm is employed to estimate different mixture models for raw single and multi-band electro-optical Infra Red (IF) data"--Abstract, page iii.

Advisor(s)

Agarwal, Sanjeev, 1971-

Committee Member(s)

Samaranayake, V. A.
Stanley, R. Joe

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Publisher

University of Missouri--Rolla

Publication Date

Spring 2006

Pagination

ix, 108 pages

Rights

© 2006 Ritesh Ganju, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Subject Headings

Expectation-maximization algorithmsGaussian distributionLand mines -- Detection -- Mathematical modelsPattern recognition systems -- Mathematical models

Thesis Number

T 9074

Print OCLC #

123550980

Recommended Citation

Ganju, Ritesh, "Expectation maximization and its application in modeling, segmentation and anomaly detection" (2006). Masters Theses. 5870.
https://scholarsmine.mst.edu/masters_theses/5870