6533b829fe1ef96bd128aef6

RESEARCH PRODUCT

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

Sara TaskinenJari MiettinenKlaus Nordhausen

subject

Statistics and ProbabilityComputer scienceJADE (programming language)02 engineering and technologyLatent variableMachine learningcomputer.software_genre01 natural sciencesBlind signal separation010104 statistics & probabilityMatrix (mathematics)nonstationary source separationMixing (mathematics)0202 electrical engineering electronic engineering information engineeringsecond order source separation0101 mathematicslcsh:Statisticslcsh:HA1-4737computer.programming_languageta113Signal processingta112matematiikkamultivariate time seriesmathematicsbusiness.industryEstimator020206 networking & telecommunicationsriippumattomien komponenttien analyysiindependent component analysis; multivariate time series; nonstationary source separation; performance indices; second order source separationIndependent component analysisperformance indicesstatisticsindependent component analysisArtificial intelligenceStatistics Probability and UncertaintybusinesscomputerAlgorithmSoftware

description

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. peerReviewed

yearjournalcountryeditionlanguage
2017-01-11Journal of Statistical Software
10.18637/jss.v076.i02https://www.jstatsoft.org/index.php/jss/article/view/2993