{"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/efficient-metric-learning-for-the-analysis-of","title":"Efficient Metric Learning for the Analysis of Motion Data","arxiv_id":"1610.05083","date":"2016-10-17","proceeding":null,"authors":["Babak Hosseini","Barbara Hammer"],"abstract":"We investigate metric learning in the context of dynamic time warping (DTW),\nthe by far most popular dissimilarity measure used for the comparison and\nanalysis of motion capture data. While metric learning enables a\nproblem-adapted representation of data, the majority of methods has been\nproposed for vectorial data only. In this contribution, we extend the popular\nprinciple offered by the large margin nearest neighbors learner (LMNN) to DTW\nby treating the resulting component-wise dissimilarity values as features. We\ndemonstrate that this principle greatly enhances the classification accuracy in\nseveral benchmarks. Further, we show that recent auxiliary concepts such as\nmetric regularization can be transferred from the vectorial case to\ncomponent-wise DTW in a similar way. We illustrate that metric regularization\nconstitutes a crucial prerequisite for the interpretation of the resulting\nrelevance profiles.","url_abs":"http://arxiv.org/abs/1610.05083v3","url_pdf":"http://arxiv.org/pdf/1610.05083v3.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":"efficient-metric-learning-for-the-analysis-of","repo_url":"https://github.com/bab-git/dist-LMNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dynamic-time-warping","task_name":"Dynamic Time Warping"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"metric-learning","task_name":"Metric Learning"}],"methods":[{"method_slug":"dtw","method_name":"DTW"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}