Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/compute_alphas.R

Using the estimates of the independent components, the function computes for a given set of nonlinearities, the quantities (alphas). Alphas determine the choices of the nonlinearities and in which order the nonlinearities are used in the adaptive deflation-based FastICA method.

1 | ```
compute_alphas(Z, gs=gf, dgs=dgf, name=gnames)
``` |

`Z` |
a numeric matrix of the estimated independent components, which should be standardized so that the mean is zero and the covariance matrix is the identity matrix. |

`gs` |
a vector of functions containing the nonlinearities. |

`dgs` |
a vector of functions containing the first derivatives of the nonlinearities. |

`name` |
a vector of strings containing the names of the nonlinearities. |

See the references.

A matrix where the ith row gives the estimates of alphas for the ith nonlinearity and the jth column corresponds to the jth component of `Z`

.

Jari Miettinen

Hyvarinen, A. and Oja, E. (1997), A fast fixed-point algorithm for independent component analysis, *Neural Computation*, vol. 9, 1483–1492.

Nordhausen, K., Ilmonen, P., Mandal, A., Oja, H. and Ollila, E. (2011), Deflation-based FastICA reloaded,
in *Proc. "19th European Signal Processing Conference 2011 (EUSIPCO 2011)",* Barcelona, 1854–1858.

Miettinen, J., Nordhausen, K., Oja, H. and Taskinen, S. (2014), Deflation-based FastICA with adaptive choices of nonlinearities, *IEEE Transactions on Signal Processing*, 62(21), 5716–5724.

fICA, nonlinearities, FOBI, k_JADE

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