B0789
Title: Blind source separation based on joint diagonalization in R: The packages JADE and BSSasymp
Authors: Jari Miettinen - University of Jyvaskyla (Finland)
Klaus Nordhausen - University of Jyvaskyla (Finland) [presenting]
Sara Taskinen - University of Jyvaskyla (Finland)
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 is assumed 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. 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 the package JADE. Both packages and their underlying methodology are introduced using simulated and real data.
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