Date Approved
9-23-2024
Embargo Period
9-24-2026
Document Type
Thesis
Degree Name
Master of Arts (M.A.)
Department
Mathematics
College
College of Science & Mathematics
Advisor
Nasrine Bendjilali, Ph.D.
Committee Member 1
Thanh Nguyen, Ph.D.
Committee Member 2
Olcay Ilicasu, Ph.D.
Keywords
deep learning; K-Means clustering; kernel principal component analysis; multilayer perceptron; neural networks; principal component analysis
Subject(s)
Neural networks (Computer Science); Computer architecture
Disciplines
Applied Mathematics | Computer Sciences
Abstract
Neural networks have proven to be powerful tools for modeling a wide range of problems across applications. However, one of the challenges in implementing a neural network model lies in determining the neural network architecture, i.e. the appropriate number of hidden layers and the number of neurons per hidden layer. It has been suggested that one way to determine the number of hidden layers is by using information on the variability captured by each principal component. In this research, we expand on this idea and propose a new approach to determine the neural network architecture for a multilayer perceptron used to classify data with a large number of attributes. The proposed approach makes use of kernel principal component analysis and clustering to determine the appropriate number of hidden layers and neurons per hidden layer. This methodology was tested using two datasets: the Parkinson’s Disease Classification dataset with binary outcomes, and the Gas Sensor Array Drift dataset with six output classes. The application of the proposed method suggests a three-hidden-layer architecture for the Parkinson’s Disease Classification dataset, achieving an average validation accuracy of 89%, while a two-hidden-layer architecture is recommended for classifying the Gas Sensor Array Drift dataset, achieving an average validation accuracy of 98%. Performance comparisons between various models and architectures are presented.
Recommended Citation
Simcox, Saige, "OPTIMIZING NEURAL NETWORK ARCHITECTURE USING KERNEL PRINCIPAL COMPONENT ANALYSIS" (2024). Theses and Dissertations. 3294.
https://rdw.rowan.edu/etd/3294
