Adaptive local Principal Component Analysis improves the clustering of high-dimensional data
Migenda N, Möller R, Schenck W (2024)
Pattern Recognition 146: 110030.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Migenda, Nico;
Möller, RalfUniBi
;
Schenck, Wolfram
![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
Einrichtung
Abstract / Bemerkung
In local Principal Component Analysis (PCA), a distribution is approximated by multiple units, each repre-senting a local region by a hyper-ellipsoid obtained through PCA. We present an extension for local PCA which adaptively adjusts both the learning rate of each unit and the potential function which guides the competition between the local units. Our local PCA method is an online neural network method where unit centers and shapes are modified after the presentation of each data point. For several benchmark distributions, we demonstrate that our method improves the overall quality of clustering, especially for high-dimensional distributions where many conventional methods do not perform satisfactorily. Our online method is also well suited for the processing of streaming data: The two adaptive mechanisms lead to a quick reorganization of the clustering when the underlying distribution changes.
Stichworte
High-dimensional clustering;
Potential function;
Adaptive learning rate;
Ranking criteria;
Neural network-based PCA;
Mixture PCA;
Local PCA
Erscheinungsjahr
2024
Zeitschriftentitel
Pattern Recognition
Band
146
Art.-Nr.
110030
ISSN
0031-3203
eISSN
1873-5142
Page URI
https://pub.uni-bielefeld.de/record/2984892
Zitieren
Migenda N, Möller R, Schenck W. Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition. 2024;146: 110030.
Migenda, N., Möller, R., & Schenck, W. (2024). Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition, 146, 110030. https://doi.org/10.1016/j.patcog.2023.110030
Migenda, Nico, Möller, Ralf, and Schenck, Wolfram. 2024. “Adaptive local Principal Component Analysis improves the clustering of high-dimensional data”. Pattern Recognition 146: 110030.
Migenda, N., Möller, R., and Schenck, W. (2024). Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition 146:110030.
Migenda, N., Möller, R., & Schenck, W., 2024. Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition, 146: 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, : 110030.
Migenda, N., Möller, R., Schenck, W.: Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition. 146, : 110030 (2024).
Migenda, Nico, Möller, Ralf, and Schenck, Wolfram. “Adaptive local Principal Component Analysis improves the clustering of high-dimensional data”. Pattern Recognition 146 (2024): 110030.
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