Nonnegative graph embedding induced unsupervised feature selection

Recently, many unsupervised feature selection (UFS) methods have been developed due to their effectiveness in selecting valuable features to improve and accelerate the subsequent learning tasks. However, most existing UFS methods suffer from the following three drawbacks: (1) They usually ignore the...

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Veröffentlicht in:	Expert systems with applications Jg. 282; S. 127664
Hauptverfasser:	Mi, Yong, Chen, Hongmei, Yuan, Zhong, Luo, Chuan, Horng, Shi-Jinn, Li, Tianrui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 05.07.2025
Schlagworte:	Low computational complexity Nonnegative attribute One-step way Unsupervised feature selection ℓ2,0norm Unsupervised feature selection One-step way ℓ2,0norm Nonnegative attribute Low computational complexity
ISSN:	0957-4174
Online-Zugang:	Volltext
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