Principal Component Analysis: A Primer
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.
John Pike (Thesis Director)
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Original document was submitted as an Honors Program requirement. Copyright is held by the author.
Sullivan, Simon. (2017). Principal Component Analysis: A Primer. In BSU Honors Program Theses and Projects. Item 237. Available at: http://vc.bridgew.edu/honors_proj/237
Copyright © 2017 Simon Sullivan
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