{"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/incremental-sparse-bayesian-ordinal","title":"Incremental Sparse Bayesian Ordinal Regression","arxiv_id":"1806.06553","date":"2018-06-18","proceeding":null,"authors":["Chang Li","Maarten de Rijke"],"abstract":"Ordinal Regression (OR) aims to model the ordering information between\ndifferent data categories, which is a crucial topic in multi-label learning. An\nimportant class of approaches to OR models the problem as a linear combination\nof basis functions that map features to a high dimensional non-linear space.\nHowever, most of the basis function-based algorithms are time consuming. We\npropose an incremental sparse Bayesian approach to OR tasks and introduce an\nalgorithm to sequentially learn the relevant basis functions in the ordinal\nscenario. Our method, called Incremental Sparse Bayesian Ordinal Regression\n(ISBOR), automatically optimizes the hyper-parameters via the type-II maximum\nlikelihood method. By exploiting fast marginal likelihood optimization, ISBOR\ncan avoid big matrix inverses, which is the main bottleneck in applying basis\nfunction-based algorithms to OR tasks on large-scale datasets. We show that\nISBOR can make accurate predictions with parsimonious basis functions while\noffering automatic estimates of the prediction uncertainty. Extensive\nexperiments on synthetic and real word datasets demonstrate the efficiency and\neffectiveness of ISBOR compared to other basis function-based OR approaches.","url_abs":"http://arxiv.org/abs/1806.06553v1","url_pdf":"http://arxiv.org/pdf/1806.06553v1.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":"incremental-sparse-bayesian-ordinal","repo_url":"https://github.com/chang-li/SBOR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"multi-label-learning","task_name":"Multi-Label Learning"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}