gmix_deflate: EM Baggenstoss: gmix_deflate

In Mthrun/AdaptGauss2D: Gaussian Mixture Modeling in 2D

EM Baggenstoss: gmix_deflate

Description

Subroutine to update Gaussian mixture (5 Operations): 2. Pruning modes (gmix_deflate.R) The method gmix_deflate.R is killing weak modes (a mode is another name for one of the L mixture components). A weak mode is found by testing the correct mode entry of weight vector wts to see if it falls below a threshold. Only one mode per run can be killed. See the matlab documentation for more information.

Usage

gmix_deflate(Parm,MinWeight1,MinWeightAll,Verbose=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.
MinWeight1	Numerical value: First mode below this threshold is killed.
MinWeightAll	Numerical value: All modes below this threshold are killed.
Verbose	Optional: Don't print comments. Verbose=1 print them. 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.