{"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/non-negative-factorization-of-the-occurrence","title":"Non-negative Factorization of the Occurrence Tensor from Financial Contracts","arxiv_id":"1612.03350","date":"2016-12-10","proceeding":null,"authors":["Zheng Xu","Furong Huang","Louiqa Raschid","Tom Goldstein"],"abstract":"We propose an algorithm for the non-negative factorization of an occurrence\ntensor built from heterogeneous networks. We use l0 norm to model sparse errors\nover discrete values (occurrences), and use decomposed factors to model the\nembedded groups of nodes. An efficient splitting method is developed to\noptimize the nonconvex and nonsmooth objective. We study both synthetic\nproblems and a new dataset built from financial documents, resMBS.","url_abs":"http://arxiv.org/abs/1612.03350v1","url_pdf":"http://arxiv.org/pdf/1612.03350v1.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":"non-negative-factorization-of-the-occurrence","repo_url":"https://github.com/nightldj/tensor_notf","is_official":0,"mentioned_in_paper":0,"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}