{"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/streaming-kernel-pca-with-tildeosqrtn-random","title":"Streaming Kernel PCA with $\\tilde{O}(\\sqrt{n})$ Random Features","arxiv_id":"1808.00934","date":"2018-08-02","proceeding":null,"authors":["Enayat Ullah","Poorya Mianjy","Teodor V. Marinov","Raman Arora"],"abstract":"We study the statistical and computational aspects of kernel principal\ncomponent analysis using random Fourier features and show that under mild\nassumptions, $O(\\sqrt{n} \\log n)$ features suffices to achieve\n$O(1/\\epsilon^2)$ sample complexity. Furthermore, we give a memory efficient\nstreaming algorithm based on classical Oja's algorithm that achieves this rate.","url_abs":"http://arxiv.org/abs/1808.00934v2","url_pdf":"http://arxiv.org/pdf/1808.00934v2.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":"streaming-kernel-pca-with-tildeosqrtn-random","repo_url":"https://github.com/r3831/SAKPCA","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1808.00934","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}