The R Journal: accepted article

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fICA: FastICA Algorithms and Their Improved Variants PDF download
Jari Miettinen, Klaus Nordhausen and Sara Taskinen

Abstract In independent component analysis (ICA) one searches for mutually independent non gaussian latent variables when the components of the multivariate data are assumed to be linear combinations of them. Arguably, the most popular method to perform ICA is FastICA. There are two classical versions, the deflation-based FastICA where the components are found one by one, and the symmetric FastICA where the components are found simultaneously. These methods have been implemented previously in two R packages, fastICA and ica. We present the R package fICA and compare it to the other packages. The additional features of the package include for example optimization of the extraction order in the deflation-based version, possibility to use any nonlinearity function, and improvement to convergence of the deflation-based algorithm. The usage of the package is demonstrated by applying it to a real ECG data of a pregnant woman.

Received: 2017-09-21; online 2018-12-07, supplementary material, (1.1 Kb)
CRAN packages: fastICA, ica, fICA, BSSasymp
CRAN Task Views implied by cited CRAN packages: Psychometrics, ChemPhys, Multivariate


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-046,
  author = {Jari Miettinen and Klaus Nordhausen and Sara Taskinen},
  title = {{fICA: FastICA Algorithms and Their Improved Variants}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-046},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-046/index.html}
}