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Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp

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  • Miettinen, Jari
  • Nordhausen, Klaus
  • Taskinen, Sara

Abstract

Blind source separation (BSS) is a well-known signal processing tool which is used to solve practical data analysis problems in various fields of science. In BSS, we assume that the observed data consists of linear mixtures of latent variables. The mixing system and the distributions of the latent variables are unknown. The aim is to find an estimate of an unmixing matrix which then transforms the observed data back to latent sources. In this paper we present the R packages JADE and BSSasymp. The package JADE offers several BSS methods which are based on joint diagonalization. Package BSSasymp contains functions for computing the asymptotic covariance matrices as well as their data-based estimates for most of the BSS estimators included in package JADE. Several simulated and real datasets are used to illustrate the functions in these two packages.

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  • Miettinen, Jari & Nordhausen, Klaus & Taskinen, Sara, 2017. "Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i02).
    • Handle: RePEc:jss:jstsof:v:076:i02
    DOI: http://hdl.handle.net/10.18637/jss.v076.i02
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    References listed on IDEAS

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    1. Jari Miettinen & Katrin Illner & Klaus Nordhausen & Hannu Oja & Sara Taskinen & Fabian J. Theis, 2016. "Separation of Uncorrelated Stationary time series using Autocovariance Matrices," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 337-354, May.
    2. Klaus Nordhausen, 2014. "On robustifying some second order blind source separation methods for nonstationary time series," Statistical Papers, Springer, vol. 55(1), pages 141-156, February.
    3. Klaus Nordhausen & David E. Tyler, 2015. "A cautionary note on robust covariance plug-in methods," Biometrika, Biometrika Trust, vol. 102(3), pages 573-588.
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    1. Joni Virta & Niko Lietzén & Pauliina Ilmonen & Klaus Nordhausen, 2021. "Fast tensorial JADE," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 164-187, March.
    2. Claudia Cappello & Sandra De Iaco & Monica Palma, 2022. "Computational advances for spatio-temporal multivariate environmental models," Computational Statistics, Springer, vol. 37(2), pages 651-670, April.
    3. Jari Miettinen & Markus Matilainen & Klaus Nordhausen & Sara Taskinen, 2020. "Extracting Conditionally Heteroskedastic Components using Independent Component Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 293-311, March.
    4. Virta, Joni & Li, Bing & Nordhausen, Klaus & Oja, Hannu, 2020. "Independent component analysis for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    5. François Bachoc & Marc G Genton & Klaus Nordhausen & Anne Ruiz-Gazen & Joni Virta, 2020. "Spatial blind source separation," Biometrika, Biometrika Trust, vol. 107(3), pages 627-646.
    6. Lee, Seonjoo & Shen, Haipeng & Truong, Young, 2021. "Sampling properties of color Independent Component Analysis," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
    7. Nordhausen, Klaus & Ruiz-Gazen, Anne, 2022. "On the usage of joint diagonalization in multivariate statistics," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    8. Virta, Joni & Lietzén, Niko & Viitasaari, Lauri & Ilmonen, Pauliina, 2024. "Latent model extreme value index estimation," Journal of Multivariate Analysis, Elsevier, vol. 202(C).

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