{"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/the-low-rank-hurdle-model","title":"The low-rank hurdle model","arxiv_id":"1709.01860","date":"2017-09-06","proceeding":null,"authors":["Christopher Dienes"],"abstract":"A composite loss framework is proposed for low-rank modeling of data\nconsisting of interesting and common values, such as excess zeros or missing\nvalues. The methodology is motivated by the generalized low-rank framework and\nthe hurdle method which is commonly used to analyze zero-inflated counts. The\nmodel is demonstrated on a manufacturing data set and applied to the problem of\nmissing value imputation.","url_abs":"http://arxiv.org/abs/1709.01860v1","url_pdf":"http://arxiv.org/pdf/1709.01860v1.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":"the-low-rank-hurdle-model","repo_url":"https://github.com/ChrisDienes/hurdle_pca","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"imputation","task_name":"Imputation"},{"task_slug":"missing-values","task_name":"Missing Values"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}