Resumen de A showcase of the use of autoencoders in feature learning applications
Francisco David Charte Luque, Francisco Charte, María de Jesús Ruiz Recendiz, Francisco Herrera
Autoencoders are techniques for data representation learningbased on artificial neural networks. Differently to other feature learning methods which may be focused on finding specific transformations of the feature space, they can be adapted to fulfill many purposes, such as data visualization, denoising, anomaly detection and semantic hashing.This work presents these applications and provides details on howautoencoders can perform them, including code samples making use of an R package with an easy-to-use interface for autoencoder design and training, ruta. Along the way, the explanations on how each learning task has been achieved are provided with the aim to help the reader design their own autoencoders for these or other objectives.
