@article {783,
	title = {An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges},
	journal = {Neurocomputing},
	volume = {404},
	year = {2020},
	note = {TIN2015-68854-R; TIN2017-89517-P; DeepSCOP Ayudas Fundaci{\'o}n BBVA a Equipos de Investigaci{\'o}n Cient{\'\i}fica en Big Data 2018},
	pages = {93-107},
	abstract = {In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, classification performance can improve if the data is mapped to a space where classes are easily separated, and regression can be facilitated by finding a manifold of data in the feature space. As a general rule, features are transformed by means of statistical methods such as principal component analysis, or manifold learning techniques such as Isomap or locally linear embedding. From a plethora of representation learning methods, one of the most versatile tools is the autoencoder. In this paper we aim to demonstrate how to influence its learned representations to achieve the desired learning behavior. To this end, we present a series of learning tasks: data embedding for visualization, image denoising, semantic hashing, detection of abnormal behaviors and instance generation. We model them from the representation learning perspective, following the state of the art methodologies in each field. A solution is proposed for each task employing autoencoders as the only learning method. The theoretical developments are put into practice using a selection of datasets for the different problems and implementing each solution, followed by a discussion of the results in each case study and a brief explanation of other six learning applications. We also explore the current challenges and approaches to explainability in the context of autoencoders. All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.},
	keywords = {Autoencoders, Deep learning, Feature extraction, Representation learning},
	doi = {https://doi.org/10.1016/j.neucom.2020.04.057},
	author = {David Charte and Francisco Charte and M. J. del Jesus and F. Herrera}
}
@article {774,
	title = {Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines},
	journal = {Information Fusion},
	volume = {54},
	year = {2020},
	note = {TIN2015-68454-R},
	month = {02/2020},
	pages = {44-60},
	abstract = {Classifying data patterns is one of the most recurrent applications in machine learning. The number of input features influences the predictive performance of many classification models. Most classifiers work with high-dimensional spaces. Therefore, there is a great interest in facing the task of reducing the input space. Manifold learning has been shown to perform better than classical dimensionality reduction approaches, such as Principal Component Analysis and Linear Discriminant Analysis. In this sense, Autoencoders (AEs) provide an automated way of performing feature fusion, finding the best manifold to reconstruct the data. There are several models and architectures of AEs. For this reason, in this study an exhaustive analysis of the predictive performance of different AEs models with a large number of datasets is proposed, aiming to provide a set of useful guidelines. These will allow users to choose the appropriate AE model for each case, depending on data traits and the classifier to be used. A thorough empirical analysis is conducted including four AE models, four classification paradigms and a group of datasets with a variety of traits. A convenient set of rules to follow is obtained as a result.},
	keywords = {Autoencoders, classification, Deep learning, Dimensionality reduction, Feature fusion},
	doi = {https://doi.org/10.1016/j.inffus.2019.07.004},
	author = {Pulgar, Francisco J. and Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus}
}
@conference {770,
	title = {A Showcase of the Use of Autoencoders in Feature Learning Applications},
	booktitle = {International Work-Conference on the Interplay Between Natural and Artificial Computation},
	year = {2019},
	note = {TIN2015-68854-R; TIN2017-89517-P},
	month = {05/2019},
	pages = {412-421},
	abstract = {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, 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.},
	keywords = {Autoencoders, Deep learning, Feature learning},
	doi = {https://doi.org/10.1007/978-3-030-19651-6_40},
	author = {David Charte and Francisco Charte and M. J. del Jesus and F. Herrera}
}
