Embedded stacked group sparse autoencoder ensemble with L1 regularization and manifold reduction

Learning useful representations from original features is a key issue in classification tasks. Stacked autoencoders (SAEs) are easy to understand and realize, and they are powerful tools that learn deep features from original features, so they are popular for classification problems. The deep featur...

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Vydané v:	Applied soft computing Ročník 101; s. 107003
Hlavní autori:	Li, Yongming, Lei, Yan, Wang, Pin, Jiang, Mingfeng, Liu, Yuchuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.03.2021
Predmet:	Embedded deep learning Embedded stacked group sparse autoencoder (ESGSAE) Ensemble learning Feature fusion Weighted local discriminant preservation projection (w_LPPD) Embedded stacked group sparse autoencoder (ESGSAE) Embedded deep learning Feature fusion Ensemble learning Weighted local discriminant preservation projection (w_LPPD)
ISSN:	1568-4946, 1872-9681
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