FICA: FastICA algorithms and their improved variants

Jari Miettinen, Klaus Nordhausen, Sara Taskinen

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
58 Downloads (Pure)

Abstract

In independent component analysis (ICA) one searches for mutually independent nongaussian 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. Additional features in fICA include 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 the real ECG data of a pregnant woman.

Original languageEnglish
Pages (from-to)148-158
Number of pages11
JournalR JOURNAL
Volume10
Issue number2
DOIs
Publication statusPublished - 1 Jan 2018
MoE publication typeA1 Journal article-refereed

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