{"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/multi-view-factorization-machines","title":"Multi-View Factorization Machines","arxiv_id":"1506.01110","date":"2015-06-03","proceeding":null,"authors":["Bokai Cao","Hucheng Zhou","Guoqiang Li","Philip S. Yu"],"abstract":"For a learning task, data can usually be collected from different sources or\nbe represented from multiple views. For example, laboratory results from\ndifferent medical examinations are available for disease diagnosis, and each of\nthem can only reflect the health state of a person from a particular\naspect/view. Therefore, different views provide complementary information for\nlearning tasks. An effective integration of the multi-view information is\nexpected to facilitate the learning performance. In this paper, we propose a\ngeneral predictor, named multi-view machines (MVMs), that can effectively\ninclude all the possible interactions between features from multiple views. A\njoint factorization is embedded for the full-order interaction parameters which\nallows parameter estimation under sparsity. Moreover, MVMs can work in\nconjunction with different loss functions for a variety of machine learning\ntasks. A stochastic gradient descent method is presented to learn the MVM\nmodel. We further illustrate the advantages of MVMs through comparison with\nother methods for multi-view classification, including support vector machines\n(SVMs), support tensor machines (STMs) and factorization machines (FMs).","url_abs":"http://arxiv.org/abs/1506.01110v2","url_pdf":"http://arxiv.org/pdf/1506.01110v2.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":"multi-view-factorization-machines","repo_url":"https://github.com/cloudml/zen","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}