gmix_merge: EM Baggenstoss: gmix_merge
| gmix_merge | R Documentation |
EM Baggenstoss: gmix_merge
Description
Subroutine to update Gaussian mixture (5 Operations): 3. Merging modes (gmix_merge.R) This method creates a single mode from two nearly identical modes. The closeness of two modes is determined by mode_dist.R. See the matlab documentation for more information.
Usage
gmix_merge(Parm,MaxCloseness, 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$weight [1, 1:NMODE] Numerical matrix with weights for each mean. |
MaxCloseness |
Optional: Numerical value: Maximum mode closeness. Use more negative values to promote mode consolidation. Use higher values for larger dimension (suggest -0.5 times DIM). Default=-2 * DIM. |
Verbose |
Optional: Print some outputs. Default=0. |
Value
List with one named element parm, which is a nested list
Parm |
Nested list with parameters for GMM. Parm$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.
