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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