{"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/anomaly-detection-via-oversampling-principal","title":"Anomaly Detection via oversampling Principal Component Analysis","arxiv_id":null,"date":"2013-07-01","proceeding":"11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems 2013 7","authors":["Yi-Ren Yeh","Zheng-Yi Lee","Yuh-Jye Lee"],"abstract":"Abstract Outlier detection is an important issue in data mining and has been studied\r\nin different research areas. It can be used for detecting the small amount of deviated\r\ndata. In this article, we use “Leave One Out” procedure to check each individual\r\npoint the “with or without” effect on the variation of principal directions. Based on\r\nthis idea, an over-sampling principal component analysis outlier detection method\r\nis proposed for emphasizing the influence of an abnormal instance (or an outlier).\r\nExcept for identifying the suspicious outliers, we also design an on-line anomaly\r\ndetection to detect the new arriving anomaly. In addition, we also study the quick\r\nupdating of the principal directions for the effective computation and satisfying the\r\non-line detecting demand. Numerical experiments show that our proposed method\r\nis effective in computation time and anomaly detection.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-642-00909-9_43","url_pdf":"https://github.com/SohanLalYadav2304/Anomaly-detection-via-oversampling-principal-component-analysis/blob/main/C18_Anomaly%20Detection%20via%20Over-sampling%20Principal%20Component%20Analysis.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":"anomaly-detection-via-oversampling-principal","repo_url":"https://github.com/SohanLalYadav2304/Anomaly-detection-via-oversampling-principal-component-analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[{"method_slug":"pca","method_name":"PCA"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-kdd-99","task":"Anomaly Detection","dataset":"kdd 99","model":"PCA via oversampling","rank_in_archive_order":1,"of":1,"metrics":{"AUCROC":"0.99"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}