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 itself.
Best, Adrin. On Tue, 22 Jan 2019 at 10:57 Daniel López-Sánchez <l...@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> > l...@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@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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