Hi,
I think some of the algorithms already offer this (Laplacian Eigenmaps for
instance).
I'm -1 for LLE as LLE does not compute distances, but weights based on the
points directly.
Matthieu
2011/9/21 Jacob VanderPlas <[email protected]>
> Hello,
> I recently was contacted by someone interested in using manifold
> learning methods on abstract metric spaces: that is, the training data
> is a matrix of pairwise distances rather than a set of points. It would
> be fairly straightforward to implement this for basic LLE and Isomap,
> and could probably be done for the other manifold methods as well. Two
> questions:
> 1) does this seem like a feature worth including in scikit-learn? Are
> there common use-cases people can think of?
> 2) any ideas about the best interface to allow this? Because the format
> of the input is so different from the normal use-case, it may be best to
> make it a separate estimator. Perhaps `MetricLLE`, `MetricIsomap` or
> something similar. Another option would be to have a keyword similar to
> the `kernel='precomputed'` option in `KernelPCA`.
> Any thoughts?
> Jake
>
>
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All the data continuously generated in your IT infrastructure contains a
definitive record of customers, application performance, security
threats, fraudulent activity and more. Splunk takes this data and makes
sense of it. Business sense. IT sense. Common sense.
http://p.sf.net/sfu/splunk-d2dcopy1
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