{"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/kernel-treelets","title":"Kernel Treelets","arxiv_id":"1812.04808","date":"2018-12-12","proceeding":null,"authors":["Hedi Xia","Hector D. Ceniceros"],"abstract":"A new method for hierarchical clustering is presented. It combines treelets,\na particular multiscale decomposition of data, with a projection on a\nreproducing kernel Hilbert space. The proposed approach, called kernel treelets\n(KT), effectively substitutes the correlation coefficient matrix used in\ntreelets with a symmetric, positive semi-definite matrix efficiently\nconstructed from a kernel function. Unlike most clustering methods, which\nrequire data sets to be numeric, KT can be applied to more general data and\nyield a multi-resolution sequence of basis on the data directly in feature\nspace. The effectiveness and potential of KT in clustering analysis is\nillustrated with some examples.","url_abs":"http://arxiv.org/abs/1812.04808v1","url_pdf":"http://arxiv.org/pdf/1812.04808v1.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":"kernel-treelets","repo_url":"https://github.com/hedixia/KernelTreelets_v5","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"kernel-treelets","repo_url":"https://github.com/hedixia/kernel_treelet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}