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Clemson University

1. Wilson, Matthew Robert. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.

Degree: MS, Computer Engineering, 2016, Clemson University

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.' Advisors/Committee Members: Dr. Robert Schalkoff, Committee Chair, Dr. Harlan B. Russell, Dr. Yongqiang Wang.

Subjects/Keywords: Dimension reduction; feature reduction; feature selection; input reduction; Karnin Sensitivity; Principal Component Analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wilson, M. R. (2016). Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/2357

Chicago Manual of Style (16th Edition):

Wilson, Matthew Robert. “Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.” 2016. Masters Thesis, Clemson University. Accessed October 16, 2019. https://tigerprints.clemson.edu/all_theses/2357.

MLA Handbook (7th Edition):

Wilson, Matthew Robert. “Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality.” 2016. Web. 16 Oct 2019.

Vancouver:

Wilson MR. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. [Internet] [Masters thesis]. Clemson University; 2016. [cited 2019 Oct 16]. Available from: https://tigerprints.clemson.edu/all_theses/2357.

Council of Science Editors:

Wilson MR. Comparison of Karnin Sensitivity and Principal Component Analysis in Reducing Input Dimensionality. [Masters Thesis]. Clemson University; 2016. Available from: https://tigerprints.clemson.edu/all_theses/2357

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