Title

Principal Component Analysis: A Primer

Date

5-10-2017

Document Type

Thesis

Abstract

Principal Component Analysis is a linear algebra technique used to identify trends within a dataset and reduce its dimensionality. Statistics such as mean and covariance are referenced alongside matrix multiplication formulas so the reader may understand the work a computer must perform to analyze a dataset. Spectral decomposition and singular value decomposition of a matrix will be considered as the primary methods for an analysis. A data compression example will be given and applications in image processing will be discussed. To give an example of data projection for visualization purposes, the principal component analysis of a biological dataset will be demonstrated.

Comments

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Department

Mathematics

Thesis Comittee

John Pike (Thesis Director)

Kevin Rion

Laura Gross

Copyright and Permissions

Original document was submitted as an Honors Program requirement. Copyright is held by the author.

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