{"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/squeezefit-label-aware-dimensionality","title":"SqueezeFit: Label-aware dimensionality reduction by semidefinite programming","arxiv_id":"1812.02768","date":"2018-12-06","proceeding":null,"authors":["Culver McWhirter","Dustin G. Mixon","Soledad Villar"],"abstract":"Given labeled points in a high-dimensional vector space, we seek a\nlow-dimensional subspace such that projecting onto this subspace maintains some\nprescribed distance between points of differing labels. Intended applications\ninclude compressive classification. Taking inspiration from large margin\nnearest neighbor classification, this paper introduces a semidefinite\nrelaxation of this problem. Unlike its predecessors, this relaxation is\namenable to theoretical analysis, allowing us to provably recover a planted\nprojection operator from the data.","url_abs":"http://arxiv.org/abs/1812.02768v1","url_pdf":"http://arxiv.org/pdf/1812.02768v1.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":"squeezefit-label-aware-dimensionality","repo_url":"https://github.com/solevillar/SqueezeFit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}