The function performs Non-Gaussian Component Analysis as described in Blanchard et.al. (2005).
1 2 3 |
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 |
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.
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 |
...
J\"org Polzehl polzehl@wias-berlin.de
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.
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