[Eeglablist] AMICA number of mixture components

[Makoto Miyakoshi]

Dear Tatu,

That's a good question. I've only heard of heuristic way to determine it.
Jason once told me that start with 5 or 6 models, and if you find one or
two models that does not account so much data (you can check it with amica
utility tools to see which model explains which part of data) remove
them... does that make sense?

Makoto


On Wed, Jan 20, 2016 at 7:35 PM, Tatu Huovilainen <
Tatu.Huovilainen at helsinki.fi> wrote:

> Hi Makoto, Dr. Palmer & eeglab list,
>
> I have a few specific questions about AMICA, that I failed to find answers
> to from previous discussions. What should I use as a criterion for choosing
> the 'num_mix_comps' parameter? I've understood that increasing the number
> will result in better model fit, but with a chance of overfitting. Is there
> a way to make an approximation of how many mixture components it's ok to
> estimate, like the k(n_channels)^2 rule for infomax? Will it cause trouble
> (besides taking much longer), given that I have enough samples to avoid
> overfitting, if a source is well approximated with 3 densities but I'm
> using, say, 6? Are there other aspects of the data that affect choosing
> this number, like sensor types or snr?
>
> Regards,
> Tatu Huovilainen
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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