{"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/ika-independent-kernel-approximator","title":"IKA: Independent Kernel Approximator","arxiv_id":"1809.01353","date":"2018-09-05","proceeding":null,"authors":["Matteo Ronchetti"],"abstract":"This paper describes a new method for low rank kernel approximation called\nIKA. The main advantage of IKA is that it produces a function $\\psi(x)$ defined\nas a linear combination of arbitrarily chosen functions. In contrast the\napproximation produced by Nystr\\\"om method is a linear combination of kernel\nevaluations. The proposed method consistently outperformed Nystr\\\"om method in\na comparison on the STL-10 dataset. Numerical results are reproducible using\nthe source code available at https://gitlab.com/matteo-ronchetti/IKA","url_abs":"http://arxiv.org/abs/1809.01353v1","url_pdf":"http://arxiv.org/pdf/1809.01353v1.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":"ika-independent-kernel-approximator","repo_url":"https://gitlab.com/matteo-ronchetti/IKA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}