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 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
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