[scikit-learn] PR #13003: [MRG] Add Tensor Sketch algorithm to Kernel Approximation module

Adrin adrin.jalali at gmail.com
Tue Jan 22 05:02:06 EST 2019

Hi Daniel,

Thanks for the note, but sometimes there can be quite some delay in us
reviewing a PR; and the discussion about a PR best should happen on the PR


On Tue, 22 Jan 2019 at 10:57 Daniel López-Sánchez <lope at usal.es> wrote:

> Dear all,
> I recently posted a PR
> <https://github.com/scikit-learn/scikit-learn/pull/13003> which adds the
> Tensor Sketch algorithm [1] to the Kernel Approximation module of
> Scikit-learn.
> I believe this new feature makes the Kernel Approximation module more
> complete by providing a data-independent method for polynomial kernel
> approximation, as the currently included methods either require access to
> training data (Nystroem) or do not work with polynomial kernels. The
> implementation has been tested to provide the same results as the original
> Matlab implementation provided by the author of [1].
> I would appreciate any feedback you can provide,
> Regards,
> [1] Pham, N., & Pagh, R. (2013, August). Fast and scalable polynomial
> kernels via explicit feature maps. In Proceedings of the 19th ACM SIGKDD
> international conference on Knowledge discovery and data mining (pp.
> 239-247). ACM.
> *Daniel López Sánchez* <https://github.com/lopelh>
> lope at usal.es / (+34) 687174328 <+34%20687%2017%2043%2028>
> BISITE Research Group (http://bisite.usal.es <http://bisite.usal.es/en>)
> Edificio I+D+i Universidad de Salamanca, C/ Espejo S/N, 37007
> Salamanca, Spain
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20190122/430290c9/attachment.html>

More information about the scikit-learn mailing list