{"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/fast-incremental-svdd-learning-algorithm-with","title":"Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel","arxiv_id":"1709.00139","date":"2017-09-01","proceeding":null,"authors":["Hansi Jiang","Haoyu Wang","Wenhao Hu","Deovrat Kakde","Arin Chaudhuri"],"abstract":"Support vector data description (SVDD) is a machine learning technique that\nis used for single-class classification and outlier detection. The idea of SVDD\nis to find a set of support vectors that defines a boundary around data. When\ndealing with online or large data, existing batch SVDD methods have to be rerun\nin each iteration. We propose an incremental learning algorithm for SVDD that\nuses the Gaussian kernel. This algorithm builds on the observation that all\nsupport vectors on the boundary have the same distance to the center of sphere\nin a higher-dimensional feature space as mapped by the Gaussian kernel\nfunction. Each iteration involves only the existing support vectors and the new\ndata point. Moreover, the algorithm is based solely on matrix manipulations;\nthe support vectors and their corresponding Lagrange multiplier $\\alpha_i$'s\nare automatically selected and determined in each iteration. It can be seen\nthat the complexity of our algorithm in each iteration is only $O(k^2)$, where\n$k$ is the number of support vectors. Experimental results on some real data\nsets indicate that FISVDD demonstrates significant gains in efficiency with\nalmost no loss in either outlier detection accuracy or objective function\nvalue.","url_abs":"http://arxiv.org/abs/1709.00139v4","url_pdf":"http://arxiv.org/pdf/1709.00139v4.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":"fast-incremental-svdd-learning-algorithm-with","repo_url":"https://github.com/hs-jiang/FISVDD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"incremental-learning","task_name":"Incremental Learning"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}