Image Classification using Functional Analysis and Neural Networks
In this thesis a number of methods for image classification are investigated. The main goal is to explore an approach based on functional data analysis and compare it with Neural Networks. The main idea is to view each image as a function and then capture the most important variation of each image class through Functional Principal Component Analysis. For comparing the methods the cifar10 dataset
