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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Applied soft computing Jg. 101; S. 107003
Hauptverfasser:	Li, Yongming, Lei, Yan, Wang, Pin, Jiang, Mingfeng, Liu, Yuchuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.03.2021
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!