A novel approach for automatic detection of linear and nonlinear dependencies between data by means of autoencoders

Autoencoders are widely used in many scientific disciplines for their good performance as so-called building blocks of deep learning. Furthermore, they have a pronounced capability for dimensionality reduction. In this paper it is shown that autoencoders can additionally be used not only to detect but to qualify dependencies among the parameters of input data sets. For doing so, a two-step approach is proposed. Herein, the identical mapping of the input data to the output layer is done with a stacked autoencoder. Evaluating respective sensitivity measures yields the sought interrelations between the input parameters, if there are any. To verify the new approach, numerical experiments are conducted with synthesized data where linear or nonlinear dependencies between the input parameters are known a priori. It is shown that the two-step approach automatically detects these dependencies for all investigated cases. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper discusses the application of autoencoders in deep learning, particularly their ability to detect and determine dependencies among parameters in input data sets. By using stacked autoencoders and sensitivity measures, these dependencies can be automatically detected.