JADE: Blind Source Separation Methods Based on Joint Diagonalization and Some BSS Performance Criteria. Cardoso’s JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given.
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Jari Miettinen and Klaus Nordhausen and Sara Taskinen: Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp (2017)
- Virta, Joni; Li, Bing; Nordhausen, Klaus; Oja, Hannu: Independent component analysis for tensor-valued data (2017)
- Taskinen, Sara; Miettinen, Jari; Nordhausen, Klaus: A more efficient second order blind identification method for separation of uncorrelated stationary time series (2016)
- Matilainen, Markus; Nordhausen, Klaus; Oja, Hannu: New independent component analysis tools for time series (2015)
- Nordhausen, Klaus: On robustifying some second order blind source separation methods for nonstationary time series (2014)
- Risk, Benjamin B.; Matteson, David S.; Ruppert, David; Eloyan, Ani; Caffo, Brian S.: An evaluation of independent component analyses with an application to resting-state fMRI (2014)
- Miettinen, Jari; Nordhausen, Klaus; Oja, Hannu; Taskinen, Sara: Statistical properties of a blind source separation estimator for stationary time series (2012)
- C. Bordier, Michel Dojat, Pierre Lafaye de Micheaux: Temporal and Spatial Independent Component Analysis for fMRI data sets embedded in a R package (2010) arXiv