Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data

Unsupervised feature extractors are known to perform an efficient and discriminative representation of data. Insight into the mappings they perform and human ability to understand them, however, remain very limited. This is especially prominent when multilayer deep learning architectures are used. T...

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Veröffentlicht in:	arXiv.org
Hauptverfasser:	Ayinde, Babajide O, Zurada, Jacek M
Format:	Paper
Sprache:	Englisch
Veröffentlicht:	Ithaca Cornell University Library, arXiv.org 25.12.2018
Schlagworte:	Deep learning Feature extraction Human performance Multilayers Regularization
ISSN:	2331-8422
Online-Zugang:	Volltext
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