{"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/robust-ordinal-embedding-from-contaminated","title":"Robust Ordinal Embedding from Contaminated Relative Comparisons","arxiv_id":"1812.01945","date":"2018-12-05","proceeding":null,"authors":["Ke Ma","Qianqian Xu","Xiaochun Cao"],"abstract":"Existing ordinal embedding methods usually follow a two-stage routine:\noutlier detection is first employed to pick out the inconsistent comparisons;\nthen an embedding is learned from the clean data. However, learning in a\nmulti-stage manner is well-known to suffer from sub-optimal solutions. In this\npaper, we propose a unified framework to jointly identify the contaminated\ncomparisons and derive reliable embeddings. The merits of our method are\nthree-fold: (1) By virtue of the proposed unified framework, the sub-optimality\nof traditional methods is largely alleviated; (2) The proposed method is aware\nof global inconsistency by minimizing a corresponding cost, while traditional\nmethods only involve local inconsistency; (3) Instead of considering the\nnuclear norm heuristics, we adopt an exact solution for rank equality\nconstraint. Our studies are supported by experiments with both simulated\nexamples and real-world data. The proposed framework provides us a promising\ntool for robust ordinal embedding from the contaminated comparisons.","url_abs":"http://arxiv.org/abs/1812.01945v1","url_pdf":"http://arxiv.org/pdf/1812.01945v1.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":"robust-ordinal-embedding-from-contaminated","repo_url":"https://github.com/alphaprime/ROE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}