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.
| Identifer | oai:union.ndltd.org:GEORGIA/oai:digitalarchive.gsu.edu:math_theses-1030 |
| Date | 16 July 2007 |
| Creators | Renkjumnong, Wasuta - |
| Publisher | Digital Archive @ GSU |
| Source Sets | Georgia State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Mathematics Theses |
Page generated in 0.0018 seconds