A Showcase of the Use of Autoencoders in Feature Learning Applications
It serves as an educational resource for practitioners looking to apply autoencoders in various domains, but it is incremental as it reviews existing methods without introducing new techniques.
This work presents a showcase of autoencoder applications in feature learning, including data visualization, denoising, anomaly detection, and semantic hashing, and provides code samples using the R package ruta to help readers design their own autoencoders.
Autoencoders are techniques for data representation learning based 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 how autoencoders can perform them, including code samples making use of an R package with an easy-to-use interface for autoencoder design and training, \texttt{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.