{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mckernel-a-library-for-approximate-kernel","title":"McKernel: A Library for Approximate Kernel Expansions in Log-linear Time","arxiv_id":"1702.08159","date":"2017-02-27","proceeding":null,"authors":["Joachim D. Curtó","Irene C. Zarza","Feng Yang","Alexander J. Smola","Fernando de la Torre","Chong-Wah Ngo","Luc van Gool"],"abstract":"Kernel Methods Next Generation (KMNG) introduces a framework to use kernel\napproximates in the mini-batch setting with SGD Optimizer as an alternative to\nDeep Learning. McKernel is a C++ library for KMNG ML Large-scale. It contains a\nCPU optimized implementation of the Fastfood algorithm that allows the\ncomputation of approximated kernel expansions in log-linear time. The algorithm\nrequires to compute the product of Walsh Hadamard Transform (WHT) matrices. A\ncache friendly SIMD Fast Walsh Hadamard Transform (FWHT) that achieves\ncompelling speed and outperforms current state-of-the-art methods has been\ndeveloped. McKernel allows to obtain non-linear classification combining\nFastfood and a linear classifier.","url_abs":"http://arxiv.org/abs/1702.08159v9","url_pdf":"http://arxiv.org/pdf/1702.08159v9.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mckernel-a-library-for-approximate-kernel","repo_url":"https://github.com/curto2/mckernel","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"mckernel","method_name":"MCKERNEL"},{"method_slug":"sgd","method_name":"SGD"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}