Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science in Operations Research
Department
Department of Operational Sciences
First Advisor
Bruce A. Cox, PhD
Abstract
The concept of Intrinsic Dimensionality (ID) is of special interest in the field of Neural Networks (NNs) since it promotes both (a) a deeper understanding of the underlying mechanisms, and (b) embraces parsimonious modeling (that is, building the right-sized model for the task) with associated benefits to processing speed and storage requirements. This thesis explores the concept of ID via two separate, but related, questions. First, we study the potential of NN ID prediction by exploiting easily obtained quantities measured on the data. We then explore NN ID as an independent concept by comparing the results of different methods for NN ID estimation.
AFIT Designator
AFIT-ENS-MS-24-M-072
Recommended Citation
Chachmo, Ori, "On Intrinsic Dimensionality of Data Sets and Neural Networks" (2024). Theses and Dissertations. 7707.
https://scholar.afit.edu/etd/7707
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.