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 language | English |
|---|---|
| Pages (from-to) | 148-158 |
| Number of pages | 11 |
| Journal | R Journal |
| Volume | 10 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Jan 2018 |
| MoE publication type | A1 Journal article-refereed |
Funding
We would like to thank the anonymous reviewer for comments. The work of Klaus Nordhausen and Sara Taskinen was supported by CRoNoS COST Action IC1408, COST (European Cooperation in Science and Technology)