Date of Award
5-2016
Document Type
Thesis
Degree Name
Master of Science (MS)
Legacy Department
Computer Engineering
Committee Member
Dr. Robert Schalkoff, Committee Chair
Committee Member
Dr. Harlan B. Russell
Committee Member
Dr. Yongqiang Wang
Abstract
Reducing the input dimensionality of large datasets for subsequent processing will allow the process to become less computationally complex and expensive. This thesis tests if Karnin sensitivity can be applied to reducing input dimensions of feed forward neural networks as well as comparing the results to the well known principal component analysis (PCA). The resulting error when reducing dimensions of inputs of various scenarios according to PCA and Karnin sensitivities are compared. After testing, Karnin was found to be able to be used to reduce input dimensions and did as well if not better than PCA in most cases. However, Karnin, like PCA, is not without its weaknesses. To cover both techniques' weaknesses, a combination of the two techniques is introduced. In the end, 'PCA chases variance while Karnin chases good mapping.'
Recommended Citation
Wilson, Matthew Robert, "Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality" (2016). All Theses. 2357.
https://tigerprints.clemson.edu/all_theses/2357