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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