Nonlinear blind source separation exploiting spatial nonstationarity
Sipilä, M., Nordhausen, K., & Taskinen, S. (2024). Nonlinear blind source separation exploiting spatial nonstationarity. Information Sciences, 665, Article 120365. https://doi.org/10.1016/j.ins.2024.120365
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Information SciencesDate
2024Copyright
© 2024 the Authors
In spatial blind source separation the observed multivariate random fields are assumed to be mixtures of latent spatially dependent random fields. The objective is to recover latent random fields by estimating the unmixing transformation. Currently, the algorithms for spatial blind source separation can only estimate linear unmixing transformations. Nonlinear blind source separation methods for spatial data are scarce. In this paper, we extend an identifiable variational autoencoder that can estimate nonlinear unmixing transformations to spatially dependent data, and demonstrate its performance for both stationary and nonstationary spatial data using simulations. In addition, we introduce scaled mean absolute Shapley additive explanations for interpreting the latent components through nonlinear mixing transformation. The spatial identifiable variational autoencoder is applied to a geochemical dataset to find the latent random fields, which are then interpreted by using the scaled mean absolute Shapley additive explanations. Finally, we illustrate how the proposed method can be used as a pre-processing method when making multivariate predictions.
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ElsevierISSN Search the Publication Forum
0020-0255Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/207421603
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This work was partly supported by the Austrian Science Fund (P31881-N32), the Research Council of Finland (453691), the HiTEc COST Action (CA21163), the Vilho, Yrjö and Kalle Väisälä Foundation, and the Kone foundation (201903741).License
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