Autoencoder in Autoencoder Networks

Modeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed autoencoder in autoencoder networks (AE2-Nets). The pr...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 35; číslo 2; s. 2263 - 2275
Hlavní autori:	Zhang, Changqing, Geng, Yu, Han, Zongbo, Liu, Yeqing, Fu, Huazhu, Hu, Qinghua
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Bidirectional encoding Coding Complementarity complete representation Correlation Data mining Degradation Encoding Feature extraction Information processing Kernel Machine learning multiview representation learning Networks Representation learning Representations
ISSN:	2162-237X, 2162-2388, 2162-2388
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