{"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/a-unified-joint-matrix-factorization","title":"A Unified Joint Matrix Factorization Framework for Data Integration","arxiv_id":"1707.08183","date":"2017-07-25","proceeding":null,"authors":["Lihua Zhang","Shihua Zhang"],"abstract":"Nonnegative matrix factorization (NMF) is a powerful tool in data exploratory\nanalysis by discovering the hidden features and part-based patterns from\nhigh-dimensional data. NMF and its variants have been successfully applied into\ndiverse fields such as pattern recognition, signal processing, data mining,\nbioinformatics and so on. Recently, NMF has been extended to analyze multiple\nmatrices simultaneously. However, a unified framework is still lacking. In this\npaper, we introduce a sparse multiple relationship data regularized joint\nmatrix factorization (JMF) framework and two adapted prediction models for\npattern recognition and data integration. Next, we present four update\nalgorithms to solve this framework. The merits and demerits of these algorithms\nare systematically explored. Furthermore, extensive computational experiments\nusing both synthetic data and real data demonstrate the effectiveness of JMF\nframework and related algorithms on pattern recognition and data mining.","url_abs":"http://arxiv.org/abs/1707.08183v1","url_pdf":"http://arxiv.org/pdf/1707.08183v1.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":"a-unified-joint-matrix-factorization","repo_url":"https://github.com/dugzzuli/jmf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-integration","task_name":"Data Integration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.08183","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}