A1 Journal article (refereed)
Nonlinear blind source separation exploiting spatial nonstationarity (2024)


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


JYU authors or editors


Publication details

All authors or editorsSipilä, Mika; Nordhausen, Klaus; Taskinen, Sara

Journal or seriesInformation Sciences

ISSN0020-0255

eISSN1872-6291

Publication year2024

Publication date28/02/2024

Volume665

Article number120365

PublisherElsevier

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1016/j.ins.2024.120365

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/93820

Web address of parallel published publication (pre-print)https://arxiv.org/abs/2311.08004


Abstract

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.


Keywordsspatial analysissignal processingindependent component analysis

Free keywordsindependent component analysis; multivariate spatial data; Shapley values; variational autoencoder


Contributing organizations


Ministry reportingYes

Reporting Year2024

Preliminary JUFO rating3


Last updated on 2024-13-05 at 18:25