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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>","short":"N. Migenda, R. Möller, W. Schenck, Pattern Recognition 146 (2024).","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>.","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>","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."},"article_number":"110030","language":[{"iso":"eng"}],"year":"2024","_id":"4050"}]