{"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/scalable-manifold-learning-for-big-data-with","title":"Scalable Manifold Learning for Big Data with Apache Spark","arxiv_id":"1808.10776","date":"2018-08-31","proceeding":null,"authors":["Frank Schoeneman","Jaroslaw Zola"],"abstract":"Non-linear spectral dimensionality reduction methods, such as Isomap, remain\nimportant technique for learning manifolds. However, due to computational\ncomplexity, exact manifold learning using Isomap is currently impossible from\nlarge-scale data. In this paper, we propose a distributed memory framework\nimplementing end-to-end exact Isomap under Apache Spark model. We show how each\ncritical step of the Isomap algorithm can be efficiently realized using basic\nSpark model, without the need to provision data in the secondary storage. We\nshow how the entire method can be implemented using PySpark, offloading compute\nintensive linear algebra routines to BLAS. Through experimental results, we\ndemonstrate excellent scalability of our method, and we show that it can\nprocess datasets orders of magnitude larger than what is currently possible,\nusing a 25-node parallel~cluster.","url_abs":"http://arxiv.org/abs/1808.10776v1","url_pdf":"http://arxiv.org/pdf/1808.10776v1.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":"scalable-manifold-learning-for-big-data-with","repo_url":"https://gitlab.com/SCoRe-Group/IsomapSpark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}