{"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-representation-based-open-set","title":"Sparse Representation-based Open Set Recognition","arxiv_id":"1705.02431","date":"2017-05-06","proceeding":null,"authors":["He Zhang","Vishal M. Patel"],"abstract":"We propose a generalized Sparse Representation- based Classification (SRC)\nalgorithm for open set recognition where not all classes presented during\ntesting are known during training. The SRC algorithm uses class reconstruction\nerrors for classification. As most of the discriminative information for open\nset recognition is hidden in the tail part of the matched and sum of\nnon-matched reconstruction error distributions, we model the tail of those two\nerror distributions using the statistical Extreme Value Theory (EVT). Then we\nsimplify the open set recognition problem into a set of hypothesis testing\nproblems. The confidence scores corresponding to the tail distributions of a\nnovel test sample are then fused to determine its identity. The effectiveness\nof the proposed method is demonstrated using four publicly available image and\nobject classification datasets and it is shown that this method can perform\nsignificantly better than many competitive open set recognition algorithms.\nCode is public available: https://github.com/hezhangsprinter/SROSR","url_abs":"http://arxiv.org/abs/1705.02431v1","url_pdf":"http://arxiv.org/pdf/1705.02431v1.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-representation-based-open-set","repo_url":"https://github.com/hezhangsprinter/SROSR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"open-set-learning","task_name":"Open Set Learning"},{"task_slug":"sparse-representation-based-classification","task_name":"Sparse Representation-based Classification"},{"task_slug":"hypothesis-testing","task_name":"Two-sample testing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.02431","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}