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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Vydané v:	Expert systems with applications Ročník 282; s. 127664
Hlavní autori:	Mi, Yong, Chen, Hongmei, Yuan, Zhong, Luo, Chuan, Horng, Shi-Jinn, Li, Tianrui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 05.07.2025
Predmet:	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
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