Adaptive local Principal Component Analysis improves the clustering of high-dimensional data
N. Migenda, R. Möller, W. Schenck, Pattern Recognition 146 (2024).
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Pattern Recognition
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146
Artikelnummer
110030
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Migenda, Nico ; Möller, Ralf ; Schenck, Wolfram: Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. In: Pattern Recognition Bd. 146, Elsevier BV (2024)
Migenda N, Möller R, Schenck W. Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition. 2024;146. doi:10.1016/j.patcog.2023.110030
Migenda, N., Möller, R., & Schenck, W. (2024). Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition, 146. https://doi.org/10.1016/j.patcog.2023.110030
@article{Migenda_Möller_Schenck_2024, title={Adaptive local Principal Component Analysis improves the clustering of high-dimensional data}, volume={146}, DOI={10.1016/j.patcog.2023.110030}, number={110030}, journal={Pattern Recognition}, publisher={Elsevier BV}, author={Migenda, Nico and Möller, Ralf and Schenck, Wolfram}, year={2024} }
Migenda, Nico, Ralf Möller, and Wolfram Schenck. “Adaptive Local Principal Component Analysis Improves the Clustering of High-Dimensional Data.” Pattern Recognition 146 (2024). https://doi.org/10.1016/j.patcog.2023.110030.
N. Migenda, R. Möller, and W. Schenck, “Adaptive local Principal Component Analysis improves the clustering of high-dimensional data,” Pattern Recognition, vol. 146, 2024.
Migenda, Nico, et al. “Adaptive Local Principal Component Analysis Improves the Clustering of High-Dimensional Data.” Pattern Recognition, vol. 146, 110030, Elsevier BV, 2024, doi:10.1016/j.patcog.2023.110030.
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