{"id":808,"date":"2024-12-25T07:02:45","date_gmt":"2024-12-25T07:02:45","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2024\/12\/25\/2412-18247\/"},"modified":"2024-12-25T07:02:45","modified_gmt":"2024-12-25T07:02:45","slug":"2412-18247","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2024\/12\/25\/2412-18247\/","title":{"rendered":"Fr&#8217;echet regression for multi-label feature selection with implicit regularization"},"content":{"rendered":"\n<div>Fr&#8217;echet regression for multi-label feature selection with implicit regularization<\/div>\n<p> \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2412.18247v1 Announce Type: new<br \/>\nAbstract: Fr&#8217;echet regression extends linear regression to model complex responses<br \/>\n  in metric spaces, making it particularly relevant for multi-label regression,<br \/>\n  where each instance can have multiple associated labels. However, variable<br \/>\n  selection within this framework remains underexplored. In this paper, we pro pose a novel variable selection method that employs implicit regularization<br \/>\n  instead of traditional explicit regularization approaches, which can introduce<br \/>\n  bias. Our method effectively captures nonlinear interactions between predic tors and responses while promoting model sparsity. We provide theoretical<br \/>\n  results demonstrating selection consistency and illustrate the performance of<br \/>\n  our approach through numerical examples<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Dou El Kefel Mansouri, Seif-Eddine Benkabou, Khalid Benabdeslem<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2412.18247\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Fr&#8217;echet regression for multi-label feature selection with implicit regularization arXiv:2412.18247v1 Announce Type: new Abstract: Fr&#8217;echet regression extends linear regression to model complex responses in metric spaces, making it particularly relevant for multi-label regression, where each instance can have multiple associated labels. However, variable selection within this framework remains underexplored. In this paper, we pro pose [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,187,113,112],"tags":[336,765,925],"class_list":["post-808","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-ai","category-cs-lg","category-stat-ml","tag-regression","tag-regularization","tag-selection"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/808"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=808"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/808\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=808"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=808"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=808"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}