A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines

By David Charte, Francisco Charte, Salvador Garcia, M. J. Jesús, Francisco Herrera. 2018

278 citations recorded by the source corpus.

Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these variables is also important, since performance tends to decline as the input dimensionality increases, hence the interest in using feature fusion techniques, able to produce feature sets that are more compact and higher level. A plethora of procedures to fuse original variables for producing new ones has been developed in the past decades. The most basic ones use linear combinations of the original variables, such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), while others find manifold embeddings of lower dimensionality based on non-linear combinations, such as Isomap or LLE (Linear Locally Embedding) techniques. More recently, autoencoders (AEs) have emerged as an alternative to manifold learning for conducting nonlinear feature fusion. Dozens of AE models have been proposed lately, each with its own specific traits. Although many of them can be used to generate reduced feature sets through the fusion of the original ones, there also AEs designed with other applications in mind. The goal of this paper is to provide the reader with a broad view of what an AE is, how they are used for feature fusion, a taxonomy gathering a broad range of models, and how they relate to other classical techniques. In addition, a set of didactic guidelines on how to choose the proper AE for a given task is supplied, together with a discussion of the software tools available. Finally, two case studies illustrate the usage of AEs with datasets of handwritten digits and breast cancer.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Geometry and Topology, Representation & Self-Supervised Learning

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “…eﬀective adversarial attacks or to reveal conﬁdential aspects of the model itself, some recent contributions have capitalized on the possibilities of Generative Ad-versarial Networks (GANs [364] ), Variational Autoencoders [365] and other generative models towards explaining data-based decisions.”
Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “This sensitivity is measured using Frobenius Norm of the Jacobian matrix of the encoder [19].”