Double-Layer Stacked Denoising Autoencoders for Regression
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María-Elena Fernández-García [1] ; Antonio Ros-Ros [3] ; Eloy Hontoria Hernández [1] ; Aníbal R. Figueiras-Vidal [2] ; José-Luis Sancho-Gómez [1]
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[1] Universidad Politécnica de Cartagena
Universidad Politécnica de Cartagena
Cartagena, España
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[2] Universidad Carlos III de Madrid
Universidad Carlos III de Madrid
Madrid, España
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[3] Iberian Lube Base Oils Company, S.A. Cartagena, Spain
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- Localización: Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence: 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 – June 3, 2022, Proceedings, Part II / José Manuel Ferrández Vicente (dir. congr.), José Ramón Álvarez Sánchez (dir. congr.), Félix de la Paz López (dir. congr.), Hojjat Adeli (aut.), 2022, ISBN 978-3-031-06527-9, págs. 337-345
- Idioma: inglés
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Resumen
A Stacked Denoising Autoencoder (SDAE) is a deep neural network (NN) model trained and designed in one-by-one stacked layers to reconstruct the non-noisy version of the original input data. It is an architecture used with great success in statistical pattern recognition problems.The objective of this contribution is to determine if an MSDAE can benefit from the greater capabilities of representation of features obtained when two layers are introduced in the stacking process instead of a single one. To do this, the design and performance of these machines in regression problems are presented and analyzed both in terms of error and calculation cost.The experimental results underline interesting performance capabilities for specific purposes.
