Sparse PCA via l2,p-Norm Regularization for Unsupervised Feature Selection
In the field of data mining, how to deal with high-dimensional data is an inevitable topic. Since it does not rely on labels, unsupervised feature selection has attracted a lot of attention. The performance of spectral-based unsupervised methods depends on the quality of the constructed similarity m...
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Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 4 vom: 01. Apr., Seite 5322-5328
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| 1. Verfasser: |
Li, Zhengxin
(VerfasserIn) |
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| Weitere Verfasser: |
Nie, Feiping,
Bian, Jintang,
Wu, Danyang,
Li, Xuelong |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article |
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