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Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective

Coleman, Ashley B.
http://orcid.org/0000-0001-9928-838X
http://rave.ohiolink.edu/etdc/view?acc_num=wright1452775165

Abstract Details

2015, Master of Science (MS), Wright State University, Computer Science.
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.
Pascal Hitzler, Ph.D. (Advisor)
Mateen Rizki, Ph.D. (Committee Member)
John Gallagher, Ph.D. (Committee Member)
126 p.
Computer Science
Feature Extraction; Dimensionality Reduction; Principal Component Analysis; Multi-dimensional Scaling; Isomap; Kernel Principal Component Analysis

Recommended Citations

Citations

  • Coleman, A. B. (2015). Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1452775165

    APA Style (7th edition)

  • Coleman, Ashley. Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective . 2015. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1452775165.

    MLA Style (8th edition)

  • Coleman, Ashley. "Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective ." Master's thesis, Wright State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1452775165

    Chicago Manual of Style (17th edition)

wright1452775165
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© 2015, some rights reserved.Creative Commons License Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective by Ashley B. Coleman is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.