{"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/less-but-better-generalization-enhancement-of","title":"Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin","arxiv_id":"1812.01939","date":"2018-12-05","proceeding":null,"authors":["Ke Ma","Qianqian Xu","Zhiyong Yang","Xiaochun Cao"],"abstract":"In the absence of prior knowledge, ordinal embedding methods obtain new\nrepresentation for items in a low-dimensional Euclidean space via a set of\nquadruple-wise comparisons. These ordinal comparisons often come from human\nannotators, and sufficient comparisons induce the success of classical\napproaches. However, collecting a large number of labeled data is known as a\nhard task, and most of the existing work pay little attention to the\ngeneralization ability with insufficient samples. Meanwhile, recent progress in\nlarge margin theory discloses that rather than just maximizing the minimum\nmargin, both the margin mean and variance, which characterize the margin\ndistribution, are more crucial to the overall generalization performance. To\naddress the issue of insufficient training samples, we propose a margin\ndistribution learning paradigm for ordinal embedding, entitled Distributional\nMargin based Ordinal Embedding (\\textit{DMOE}). Precisely, we first define the\nmargin for ordinal embedding problem. Secondly, we formulate a concise\nobjective function which avoids maximizing margin mean and minimizing margin\nvariance directly but exhibits the similar effect. Moreover, an Augmented\nLagrange Multiplier based algorithm is customized to seek the optimal solution\nof \\textit{DMOE} effectively. Experimental studies on both simulated and\nreal-world datasets are provided to show the effectiveness of the proposed\nalgorithm.","url_abs":"http://arxiv.org/abs/1812.01939v1","url_pdf":"http://arxiv.org/pdf/1812.01939v1.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":"less-but-better-generalization-enhancement-of","repo_url":"https://github.com/alphaprime/DMOE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}