initial.em.worker: Initialization for model-based clustering
Description
This function implements one E-step for one random initial start.
Usage
initial.em.worker(PARAM, MU = NULL)
Arguments
PARAM
an original set of parameters generated
by set.global.
MU
a center matrix with dim = $p \times K$.
Value
An initial set of parameters PARAM will be returned.
Details
This function takes X.worker from the global environment and
randomly pick $K$ of them as the centers of $K$ groups.
If MU is specified, then this MU
will be the centers.
The default identity dispersion in PARAM$SIGMA will be used.
Then, one E-step will be called to obtain the log likelihood and new
classification will be updated.
This function is used to implement the RndEM procedure for more
elaborate initialization scheme in initial.RndEM.worker.
Potentially, several random starts should be tried before running EM
algorithms. This can benefit in two aspects including:
shorter convergent iterations and better classification results.
References
High Performance Statistical Computing Website:
http://thirteen-01.stat.iastate.edu/snoweye/hpsc/
Maitra, R. (2009)
Initializing partition-optimization algorithms,
IEEE/ACM Transactions on Computational Biology and Bioinformatics,
6:1, 114-157.