2022/10/13 by Tommi Kärkkäinen, Kärkkäinen, Tommi, Jan Hänninen +1 · 1 citation
Earth and Planetary Sciences · Environmental Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Meteorological Phenomena and Simulations #Model Reduction and Neural Networks
paper · pdf · doi:10.48550/arxiv.2210.06773
openalex publication_date 2022/10/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
An additive autoencoder for dimension reduction, which is composed of a serially performed bias estimation, linear trend estimation, and nonlinear residual estimation, is proposed and analyzed. Computational experiments confirm that an autoencoder of this form, with only a shallow network to encapsulate the nonlinear behavior, is able to identify an intrinsic dimension of a dataset with a low autoencoding error. This observation leads to an investigation in which shallow and deep network structures, and how they are trained, are compared. We conclude that the deeper network structures obtain lower autoencoding errors during the identification of the intrinsic dimension. However, the detected dimension does not change compared to a shallow network.