Date of Award

7-16-2007

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematics and Statistics

First Advisor

Marina Arav - Chair

Second Advisor

Frank Hall

Third Advisor

Zhongshan Li

Fourth Advisor

Michael Stewart

Fifth Advisor

Saeid Belkasim

Abstract

The Singular Value Decomposition is one of the most useful matrix factorizations in applied linear algebra, the Principal Component Analysis has been called one of the most valuable results of applied linear algebra. How and why principal component analysis is intimately related to the technique of singular value decomposition is shown. Their properties and applications are described. Assumptions behind this techniques as well as possible extensions to overcome these limitations are considered. This understanding leads to the real world applications, in particular, image processing of neurons. Noise reduction, and edge detection of neuron images are investigated.

DOI

https://doi.org/10.57709/1059687

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