From data to insights: Evaluating Variational Autoencoders and Generative Autoencoders for Migraine Research in AI Driver Healthcare

Abstract

This study investigates the clustering performance of Variational Autoencoders (VAE) and Generative Autoencoders (GAE) on a migraine dataset. Autoencoders (AEs) are increasingly used in unsupervised learning for their ability to reduce dimensionality and capture complex data representations. In this work, both VAE and GAE models were trained under identical conditions on a migraine dataset to evaluate their effectiveness in generating latent representations for clustering