Nothing is better than asking help to find the answer by myself...
Page 47 of the technical report (tr504.pdf) deals exactly with the
problem of big datasets.
Also I found that mclust is too much for my problem, the optimum number
of Gaussian suggested is way too high. For example for one dataset
(downsampled to 1/10) it suggests 9 Gaussian, but the central 7 sum with
good approximation to a single Gaussian, so the dataset is better
decomposed into only 3 Gaussian.
I admit I'm not rigorous at all...
Bye!
mario
Mario Valle wrote:
Hi all!
I have an ordered vector of values. The distribution of these values
can be modeled by a sum of Gaussians.
So I'm using the package 'mclust' to get the Gaussians's parameters
for this 1D distribution. It works very well, but, for input sizes
above 100.000 values it starts taking really forever. Unfortunately my
dataset has around 4.6M values...
My question: is it correct to subsample my dataset taking a value
every N to make mclust happy? Or have I no alternative except using
the complete dataset?
Excuse my profound ignorance and thank for your help!
mario
--
Ing. Mario Valle
Data Analysis and Visualization Group | http://www.cscs.ch/~mvalle
Swiss National Supercomputing Centre (CSCS) | Tel: +41 (91) 610.82.60
v. Cantonale Galleria 2, 6928 Manno, Switzerland | Fax: +41 (91) 610.82.82
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