# Non-Gaussian Component Analysis

### Description

The function performs Non-Gaussian Component Analysis as described in Blanchard et.al. (2005).

### Usage

1 2 3 |

### Arguments

`data` |
Observation matrix (dimension Nxd) |

`L` |
Number basis functions in each of four classes. |

`T` |
Number of Fast ICA iterations |

`m` |
Number of non-Gaussian components. |

`eps` |
Threshold (defaults to 1.5) |

`npca` |
Reduce space to |

`filter.time` |
Choice of temporal filtering before analysis: |

`filter.space` |
Choice of spatial filtering before analysis: logical, default |

`method` |
Either |

`dg.trend` |
not yet documented |

`h.space` |
bandwidth for spatial filtering. default 3 |

`h.time` |
bandwidth for temporal filtering. default 3 |

`keepv` |
if |

`delta` |
not yet documented |

### Details

The function performs Non-Gaussian Component Analysis as described in Blanchard et.al. (2006). The procedure uses four classes of basis functions, i.e. Gauss-Power3, Hyperbolic Tangent and the real and complex part of the Fourier class. See Blanchard et.al. (2005) for details.

### Value

The function returns a list with components

`ihat ` |
Matrix containing the first m NGCA directions as columns. |

`sdev ` |
Standard deviations of the principal components of the thresholded ICA directions |

`xhat ` |
first m components of the rotated data |

`v ` |
If |

`normv ` |
If |

...

### Author(s)

J\"org Polzehl polzehl@wias-berlin.de

### References

Blanchard, G., Kawanabe, M., Sugiyama, M., Spokoiny, V. and M\"uller K.-R. (2005). In Search of Non-Gaussian Components of a High-Dimensional Distribution. Journal of Machine Learning Research. pp. 1-48.