init_gmix: EM Baggenstoss: init_gmix

In Mthrun/AdaptGauss2D: Gaussian Mixture Modeling in 2D

EM Baggenstoss: init_gmix

Description

Initialization of GMM parameters for training on data with gmix_trainscript.R

Usage

init_gmix(Data, Nmodes, MinStd, Names=NULL, RandInit=1, Verbose=0)

Arguments

Data	[1:n,1:d] Numerical matrix with n samples and d feature dimensions.
Nmodes	[1:L] Number of modes to initialize.
MinStd	Numerical vector with covariance constraints for each feature. Preventing the covariance matrix to become singular.
Names	[1:d] String vector with feature names.
RandInit	Optional: Use randomized initialization. Random=1, Nonrandom=0.
Verbose	Optional: Print Output. 1=Yes, 0=No. Default=1.

Value

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

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.

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.