Publication Date
2015
Document Type
Thesis
Committee Members
John Gallagher (Committee Member), Pascal Hitzler (Advisor), Mateen Rizki (Committee Member)
Degree Name
Master of Science (MS)
Abstract
The purpose of this paper is to determine if any of the four commonly used dimensionality reduction techniques are reliable at extracting the same features that humans perceive as distinguishable features. The four dimensionality reduction techniques that were used in this experiment were Principal Component Analysis (PCA), Multi-Dimensional Scaling (MDS), Isomap and Kernel Principal Component Analysis (KPCA). These four techniques were applied to a dataset of images that consist of five infrared military vehicles. Out of the four techniques three out of the five resulting dimensions of PCA matched a human feature. One out of five dimensions of MDS matched a human feature. Two out of five dimensions of Isomap matched a human feature. Lastly, none of the resulting dimensions of KPCA matched any of the features that humans listed. Therefore PCA was the most reliable technique for extracting the same features as humans when given a set number of images.
Page Count
126
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2015
Copyright
Copyright 2015, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
