Hi Daniel,
I think in the original scikit pull request on my PhD thesis almost 10
years ago, there may have been some Sammon mapping code. IIRC, the mapping
is really crude and not robust. I think there are other cost functions for
dimensionality reduction that are far more efficient and do not have the
same drawbacks than Sammon mapping.
I don't remember my position on PCA, I know that I had a look at it but
never implemented it. What is the purpose of implementing this one in
particular?
Cheers,
Matthieu
2016-04-25 7:59 GMT+02:00 Daniel McNeela <daniel.mcne...@gmail.com>:
> Hi All,
>
> My name is Daniel McNeela, and I am a student at UC Berkeley participating
> in Google Summer of Code 2016. I am working on the Fovea project under the
> umbrella of the International Neuroinformatics Coordinating Facility. The
> abstract for my project can be found here:
> https://summerofcode.withgoogle.com/projects/#5940697098092544
>
> To be brief, Fovea is a Python tool for visualizing dynamical systems and
> associated data, and an integral part of the back end for the software
> involves performing both linear and nonlinear dimensionality reduction on
> data sets. My project mentor would like to add scikit-learn as a dependency
> since it already has a number of manifold learning algorithms implemented.
> However, I am planning on using two additional algorithms that are not
> currently implemented in scikit-learn, namely Sammon Mapping and Principal
> Curve Analysis, and I was wondering whether the developer team would be
> interested in incorporating these two algorithms into scikit-learn's
> existing Manifold Learning package.
>
> Please let me know your thoughts. Information regarding these two
> algorithms can be found at the following links:
>
>
> http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0910/henderson.pdf
>
> https://web.stanford.edu/~hastie/Papers/Principal_Curves.pdf
>
> <https://web.stanford.edu/~hastie/Papers/Principal_Curves.pdf>
> Thanks for your time, and looking forward to hearing from you!
>
>
> - Daniel
>
>
>
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