After checking the code, the metric MDS used here is the classic MDS (the
stress function is squared-sum of the discrepancies between original
distances and computed distances). I didn't check the speed yet (currently
on road), but the implementation may benefit from using directly PCA (just
like Isomap did, as Isomap is just an classic MDS with a special input
distance matrix).
I'll try to see if I have the time to do a comparison. Perhaps the current
implementation is faster! In that case, Isomap may be enhanced by MDS.
Cheers,
Matthieu
2012/9/5 Andreas Mueller <[email protected]>
> Hi Matthieu.
> The release contains metric and non-metric MDS.
> Details can be found here:
> http://scikit-learn.org/stable/modules/manifold.html#multidimensional-scaling
> I don't think the implementation includes classical MDS,
> as that would be redundant, like you said.
>
> Hope that helps,
> Andy
>
>
>
> On 09/05/2012 07:08 AM, Matthieu Brucher wrote:
>
> Excellent work!
> I have a question on MDS. Is it the classic MDS or something else? (Asking
> the question as PCA is the classic MDS). It seems to be when the distance
> matrix is Euclidean?
>
> Cheers,
>
> Matthieu
>
> 2012/9/5 Andreas Mueller <[email protected]>
>
>> Dear fellow Pythonistas.
>> I am pleased to announce the release of scikit-learn 0.12.
>> This release adds several new features, for example
>> multidimensional scaling (MDS), multi-task Lasso
>> and multi-output decision and regression forests.
>>
>> There has also been a lot of progress in documentation
>> and ease of use.
>>
>> Details can be found on the what's new
>> <http://scikit-learn.org/stable/whats_new.html>page.
>>
>> Sources and windows binaries are available on sourceforge,
>> through pypi (http://pypi.python.org/pypi/scikit-learn/0.12) or
>> can be installed directly using pip:
>>
>> pip install -U scikit-learn
>>
>>
>> I want to thank all of the developers who made this release possible
>> and welcome our new contributors.
>>
>> Keep on learning,
>> Andy
>>
>>
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>
>
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