Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp | Miettinen | Journal of Statistical Software
Authors: Jari Miettinen, Klaus Nordhausen, Sara Taskinen
Title: Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp
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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Paper: Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp     Download PDF (Downloads: 369)
Supplements:
JADE_2.0-0.tar.gz: R source package Download (Downloads: 11; 2MB)
BSSasymp_1.2-0.tar.gz: R source package Download (Downloads: 13; 19KB)
v76i02.R: R replication code Download (Downloads: 19; 5KB)
foetal_ecg.dat: Replication data Download (Downloads: 14; 375KB)

DOI: 10.18637/jss.v076.i02

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.