Transfer robust sparse coding based on graph and joint distribution adaption for image representation

Transfer learning can transfer knowledge from a source domain to a target domain, promoting the performance of the model learned from the source data. Sparse coding can make the representation of a model more succinct and easy to manipulate. Existing transfer sparse coding methods assume the data fr...

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Veröffentlicht in:	Knowledge-based systems Jg. 147; S. 1 - 11
Hauptverfasser:	Zhao, Peng, Wang, Wei, Lu, Yijuan, Liu, Huiting, Yao, Sheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.05.2018 Elsevier Science Ltd
Schlagworte:	Algorithms Artificial intelligence Coding Data Distribution Experiments Graphical representations Image representation Information dissemination Knowledge management Knowledge sharing Learning Noise Representation Robustness Sparse coding Transfer learning Image representation Sparse coding Transfer learning
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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