[R] fitting mixture of gaussians using emclust() of mclust package
Jonathan Qiang Li
jonqli at labs.agilent.com
Thu Aug 2 12:18:30 CEST 2001
Thanks for the pointer.
BTW, does your package implement some of these variations (such as
stochastic EM, restarting EM etc.)
or is a straight EM?
Regards,
Jonathan
Christian Hennig wrote:
>
> Dear Jonathan,
>
> Chapter 12 of G. McLachlan, D. Peel "Finite Mixture Models", Wiley, NY 2000
> is devoted to this topic and contains lots of further references.
>
> Regards,
> Christian
>
> On Wed, 1 Aug 2001, Jonathan Qiang Li wrote:
>
> > Thanks for help.
> >
> > Rather than using emclust(), using me() directly with kmeans-induced
> > initial starting parameters
> > seems to work better (not sure how much since to get results I have to
> > sample the data pretty aggressively).
> >
> > But I still found that when I have data with more than 10,000 obs,
> > it takes the routine painfully long time to converge. I understand that
> > the speed of
> > convergence for EM algorithm is data-dependent and in general very slow.
> > But do people have some benchmark
> > estimate for the relationship between the sample size and the
> > computation time using R from their experience? Can also
> > some one point out some references/packages for speeding up EM,
> > especially when sample size and
> > dimension are not trivial? (not exactly a R-related question, but I
> > thought people on this list would be interested in such problems).
> >
> > Regards,
> > Jonathan
>
> ***********************************************************************
> Christian Hennig
> University of Hamburg, Faculty of Mathematics - SPST/ZMS
> (Schwerpunkt Mathematische Statistik und Stochastische Prozesse,
> Zentrum fuer Modellierung und Simulation)
> Bundesstrasse 55, D-20146 Hamburg, Germany
> Tel: x40/42838 4907, privat x40/631 62 79
> hennig at math.uni-hamburg.de, http://www.math.uni-hamburg.de/home/hennig/
> #######################################################################
> ich empfehle www.boag.de
--
Jonathan Q. Li, PhD
Agilent Technologies Laboratory
Palo Alto, California, USA
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