gmix_kurt: EM Baggenstoss: gmix_kurt

In Mthrun/AdaptGauss2D: Gaussian Mixture Modeling in 2D

EM Baggenstoss: gmix_kurt

Description

Subroutine to update Gaussian mixture (5 Operations): 4. Splitting modes (gmix_kurt.R) A split of a mode can be determined with the weighted kurtosis for each mode for one dimensional problems. For multidimensional problems the skew is used in addition to the kurtosis. See the matlab documentation for the explicit mathematical formulas and remarks.

Usage

gmix_kurt(Parm,Data,Threshold,Debug=0)

Arguments

Parm	Nested list with parameters for GMM. Features carrying permanent values. Features$name [1:d] String vector with feature names. Features$min_std [1:NMODE] Vector of covariance constraints. Modes carrying modifyable values. Modes$cholesky_covar [d*NMODE, d] Numerical matrix with NMODE many square matrices stacked vertically with the covariance matrix. Modes$mean [1:NMODE, d] Numerical matrix with nmode different means and d feature dimensions. Modes$weight [1, 1:NMODE] Numerical matrix with weights for each mean.
Data	[1:n,1:d] Numerical matrix with normalized data. N samples with DIM feature dimensions.
Threshold	Optional: Numerical value: . Default=1.0.
Debug	Optional: Print some outputs. Default=0.

Value

List with one named element parm, which is a nested list

Parm

Nested list with parameters for GMM. Parm$features carrying permanent values. Parm$features$name [1:d] String vector with feature names. Parm$features$min_std [1:NMODE] Vector of covariance constraints. Parm$modes carrying modifyable values. Parm$modes$cholesky_covar [d*NMODE, d] Numerical matrix with NMODE many square matrices stacked vertically with the covariance matrix. Parm$modes$mean [1:NMODE, d] Numerical matrix with nmode different means and d feature dimensions. Parm$modes$weight [1, 1:NMODE] Numerical matrix with weights for each mean.

Author(s)

Quirin Stier

References

Baggenstoss, Paul M., and T. E. Luginbuhl.: An EM algorithm for joint model estimation. IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), Phoenix, AZ, USA, 1999, pp. 1825-1828 vol.4, IEEE, doi:10.1109/ICASSP.1999.758276, 1999.

Mthrun/AdaptGauss2D documentation built on July 19, 2022, 3:11 a.m.