{"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/multiparameter-persistence-image-for","title":"Multiparameter Persistence Image for Topological Machine Learning","arxiv_id":null,"date":"2020-12-01","proceeding":"NeurIPS 2020 12","authors":["Mathieu Carrière","Andrew Blumberg"],"abstract":"In the last decade, there has been increasing interest in topological\ndata analysis, a new methodology for using geometric structures in\ndata for inference and learning. A central theme in the area is the\nidea of persistence, which in its most basic form studies how measures\nof shape change as a scale parameter varies. There are now a number of\nframeworks that support statistics and machine learning in this\ncontext. However, in many applications there are several different\nparameters one might wish to vary: for example, scale and density.  In\ncontrast to the one-parameter setting, techniques for applying\nstatistics and machine learning in the setting of multiparameter\npersistence are not well understood due to the lack of a concise\nrepresentation of the results.","url_abs":"http://proceedings.neurips.cc/paper/2020/hash/fdff71fcab656abfbefaabecab1a7f6d-Abstract.html","url_pdf":"http://proceedings.neurips.cc/paper/2020/file/fdff71fcab656abfbefaabecab1a7f6d-Paper.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":"multiparameter-persistence-image-for","repo_url":"https://github.com/MathieuCarriere/multipers","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"topological-data-analysis","task_name":"Topological Data Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}