[{"publication":"Pattern Recognition","publication_status":"published","project":[{"name":"Institut für Systemdynamik und Mechatronik","_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b"}],"type":"journal_article","publication_identifier":{"issn":["00313203"]},"user_id":"224375","date_created":"2023-12-18T23:37:19Z","title":"Adaptive local Principal Component Analysis improves the clustering of high-dimensional data","volume":146,"quality_controlled":"1","department":[{"_id":"103"}],"funded_apc":"1","article_number":"110030","_id":"4050","year":"2024","language":[{"iso":"eng"}],"doi":"10.1016/j.patcog.2023.110030","intvolume":"       146","date_updated":"2026-03-17T15:28:54Z","publisher":"Elsevier BV","author":[{"orcid":"0000-0002-7223-1735","full_name":"Migenda, Nico","last_name":"Migenda","first_name":"Nico","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-7223-1735/work/177074489","id":"218473"},{"first_name":"Ralf","last_name":"Möller","full_name":"Möller, Ralf"},{"last_name":"Schenck","full_name":"Schenck, Wolfram","orcid":"0000-0003-3300-2048","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-3300-2048/work/177074491","id":"224375","first_name":"Wolfram"}],"citation":{"ieee":"N. Migenda, R. Möller, and W. Schenck, “Adaptive local Principal Component Analysis improves the clustering of high-dimensional data,” <i>Pattern Recognition</i>, vol. 146, 2024.","alphadin":"<span style=\"font-variant:small-caps;\">Migenda, Nico</span> ; <span style=\"font-variant:small-caps;\">Möller, Ralf</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span>: Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. In: <i>Pattern Recognition</i> Bd. 146, Elsevier BV (2024)","bibtex":"@article{Migenda_Möller_Schenck_2024, title={Adaptive local Principal Component Analysis improves the clustering of high-dimensional data}, volume={146}, DOI={<a href=\"https://doi.org/10.1016/j.patcog.2023.110030\">10.1016/j.patcog.2023.110030</a>}, number={110030}, journal={Pattern Recognition}, publisher={Elsevier BV}, author={Migenda, Nico and Möller, Ralf and Schenck, Wolfram}, year={2024} }","apa":"Migenda, N., Möller, R., &#38; Schenck, W. (2024). Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. <i>Pattern Recognition</i>, <i>146</i>. <a href=\"https://doi.org/10.1016/j.patcog.2023.110030\">https://doi.org/10.1016/j.patcog.2023.110030</a>","chicago":"Migenda, Nico, Ralf Möller, and Wolfram Schenck. “Adaptive Local Principal Component Analysis Improves the Clustering of High-Dimensional Data.” <i>Pattern Recognition</i> 146 (2024). <a href=\"https://doi.org/10.1016/j.patcog.2023.110030\">https://doi.org/10.1016/j.patcog.2023.110030</a>.","short":"N. Migenda, R. Möller, W. Schenck, Pattern Recognition 146 (2024).","mla":"Migenda, Nico, et al. “Adaptive Local Principal Component Analysis Improves the Clustering of High-Dimensional Data.” <i>Pattern Recognition</i>, vol. 146, 110030, Elsevier BV, 2024, doi:<a href=\"https://doi.org/10.1016/j.patcog.2023.110030\">10.1016/j.patcog.2023.110030</a>.","ama":"Migenda N, Möller R, Schenck W. Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. <i>Pattern Recognition</i>. 2024;146. doi:<a href=\"https://doi.org/10.1016/j.patcog.2023.110030\">10.1016/j.patcog.2023.110030</a>"},"status":"public"}]