{"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/sparse-feature-selection-in-kernel","title":"Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring","arxiv_id":"1902.04248","date":"2019-02-12","proceeding":null,"authors":["Alexander F. Lapanowski","Irina Gaynanova"],"abstract":"We consider the two-group classification problem and propose a kernel\nclassifier based on the optimal scoring framework. Unlike previous approaches,\nwe provide theoretical guarantees on the expected risk consistency of the\nmethod. We also allow for feature selection by imposing structured sparsity\nusing weighted kernels. We propose fully-automated methods for selection of all\ntuning parameters, and in particular adapt kernel shrinkage ideas for ridge\nparameter selection. Numerical studies demonstrate the superior classification\nperformance of the proposed approach compared to existing nonparametric\nclassifiers.","url_abs":"http://arxiv.org/abs/1902.04248v1","url_pdf":"http://arxiv.org/pdf/1902.04248v1.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":"sparse-feature-selection-in-kernel","repo_url":"https://github.com/aflapan/sparseKOS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"feature-selection","task_name":"feature selection"}],"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}