Hi folks,
Nick Travers and I have been working steadily
<https://github.com/cemoody/scikit-learn/tree/cemoody/bhtsne/sklearn/manifold>
on a Barnes-Hut approximation of the t-SNE algorithm currently implemented
as a manifold learning technique. This version makes the gradient
calculation much faster, changing the computational time from O(N^2) to O(N
log N). This effectively extends t-SNE from being usable on thousands of
examples to millions of examples while only losing a little bit of
precision. t-SNE was first published in 2009
<http://lvdmaaten.github.io/tsne/> and the Barnes-Hut approximation was
introduced <http://arxiv.org/abs/1301.3342> only two years ago, but it was
accepted into publication only this year [pdf
<http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf>] making it
relatively new.
Ahead of submitting a PR, I wanted to start the process for thinking about
its inclusion (or exclusion!). It's a performance improvement to an
established and widely-used algorithm but it's also quite new.
- What do you all think?
- Is this too new to be incorporated, or does its usefulness merit
inclusion?
- We've read the Contributing <http://scikit-learn.org/stable/developers/> docs
-- what else can Nick & I do to make the PR as easy as we can for
reviewers? And looking forward, what can we do minimize code rot and ease
the maintenance burden?
Many thanks & looking forward to being part of scikit-learn community!
chris & nick
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